# 16. Learning-to-learn for neuromorphic hardware

Franz Scherr and Wolfgang Maass

Institute of Theoretical Computer Science, TU Graz

## 16.1. Status

An important goal for neuromorphic hardware is to support fast on-chip learning in the hand of a user. Two problems need to be solved for that:

(a) A sufficiently powerful learning method has to run on the chip, such as stochastic gradient descent.

(b) This on-chip learning needs to converge fast, requiring ideally just a single example (oneshot learning).

Evolution has found methods that enable brains to learn a new class from a single or very few examples. For instance, we can recognize a new face in many orientations, scales, and lighting conditions after seeing it just once, or at least after seeing it a few times. But this fast learning is supported by a long series of prior optimization processes of the neural networks in the brain during evolution, development, and prior learning. In addition, insight from cognitive science suggests that the learning and generalization capability of our brains is supported by innate knowledge, e.g. about basic properties of objects, 3D space, and physics. Hence, in contrast to most prior on-chip learning experiments in neuromorphic engineering, neural networks in the brain do not start from a tabula rasa state when they learn something new.

Learning from few examples has already been addressed in modern machine learning and AI [323]. Of particular interest for neuromorphic applications are methods that enable recurrently connected neural networks RNNs to learn from single or few examples. RNNs are usually needed for online temporal processing—an application domain of particular interest for energy-efficient neuromorphic hardware. The gold standard for RNN-learning is backpropagation through time (BPTT). While BPTT is inherently an offline learning method that appears to be off-limit for online onchip learning, it has recently been shown that BPTT can typically be approximated quite well by computationally efficient online approximations. In particular, one can port the online broadcast alignment heuristic from feedforward to recurrent neural networks [324]. In addition, one can emulate the common LSTM (long short-term memory) units of RNNs in machine learning by neuromorphic hardware-friendly adapting spiking neurons. Finally, a computationally efficient online approximation of BPTT—called e-prop—exists that also works well for recurrent networks of spiking neurons (RSNNs) with such adapting neurons [260]. The resulting algorithm for on-chip training of the weights $W_{ji}$ for neuron i to neuron j of an RSNN—for reducing some arbitrary but differentiable loss function E—takes there the form

$$ \frac{dE}{dW_{ji}} = \sum_{t} L_{j}^{t} e_{ji}^{t}. $$

The so-called learning signal $L_j^t$ at time, is some online available approximation to the derivative of the loss function E with regard to the spike output of neuron j, and the eligibility trace $e_{ji}^t$ is an online and locally computable eligibility trace. While this would usually require even more training examples than BPTT, one can speed it up substantially by optimizing the learning signal $L_j^t$ and the initial values of the synaptic weights $W_{ji}$ to enable learning from few examples for a large—in general even infinitely large—family $\mathcal{F}$ of on-chip learning tasks [325]. This can be achieved through learning-to-learn (L2L) [326]. A scheme for the application of L2L to enable fast on-chip learning is shown in figure 23.

## 16.2. Current and future challenges

The main choices that have to be made for such realization of fast on-chip learning are the choice of the family $\mathcal{F}$ of tasks, the choice of the optimization method for offline priming through the definition of hyperparameters, and the choice of the hyperparameters. Options for the latter are for example:

(a) Just the learning rate parameters of on-chip learning rules are hyperparameters.

(b) Also the values of all synaptic weights of the RSNN are hyperparameters.

(c) Only the initial values of the synaptic weights of the RSNN are hyperparameters.

(d) In addition all parameters of an auxiliary NN—the learning signal generator—that generates online learning signals $L_j^t$ for fast convergence of e-prop on the chip are hyperparameters.

Option 1 has been explored for RSNNs in [327, 328], and with an application to analog neuromorphic hardware in [329]. Option 2 is arguably the most commonly considered application of L2L in machine learning and computational neuroscience models [326, 330–334]. An attractive feature of this option for realizing fast on-chip learning is that it requires no synaptic plasticity for that. Rather, it uses hidden variables of the RNN
<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

for storing information from the few training examples that are needed for fast learning. In the case of machine learning, these hidden variables are the values of memory cells of LSTM units. In SNN these are the current values of firing thresholds of adapting neurons. An alternative is to choose only some synaptic weights to be hyperparameters, and to leave others open for fast on-chip learning [335]. Option 3 is used by the MAML approach of [336], where only very few updates of synaptic weights via BPTT are required in the inner loop of L2L. It also occurs in [325] in conjunction with option 4, see figure 24 for an illustration.

One common challenge that underlies the success of all mentioned options, is the efficacy of the training algorithm for the offline priming phase, the outer loop of L2L. While option 1 can often be carried out by gradient-free methods, the more demanding network optimizations of the other options tend to require BPTT for offline priming of the RNN.

16.3. Advances in science and technology to meet challenges

It is quite realistic to enable according to this L2L method fast on-chip learning on neuromorphic hardware.
The most demanding aspect for the hardware is to be able to run the on-chip learning algorithm that is
required. This can be implemented on most neuromorphic hardware if only simple local rules for synaptic
plasticity are required in the inner loop of L2L, as in option 1. In the case of option 2 a spike-based neuromorphic hardware just needs to be able to emulate adapting spiking neurons. This can be done for example on SpiNNaker [337] and Intel's Loihi chip [202]. Using BPTT for on-chip learning appears to be currently infeasible, but on-chip learning with e-prop is supported by SpiNNaker and the next generation of Loihi. Then option 4 can be used for enabling more powerful fast on-chip learning. The only additional requirement for the hardware is that an offline primed learning signal generator can be downloaded onto the chip (once and for all), and that the chip supports communication of its learning signal for gating local synaptic plasticity rules according to eprop.

An illustration of a sample application which becomes then realistic is shown in figure 24: on-chip learning of a new spoken command from a single example in such a way that the same command can then also be recognized under different acoustic conditions and from different speakers.

Future advances need to address the challenge of training extended learning problems during the offline phase. Besides improved gradient-based algorithms, also gradient-free training methods such as evolution strategies [338] are attractive for that. In fact, since the latter paradigm allows to employ neuromorphic hardware directly for evaluating the learning performance, this approach can benefit from the speed and efficiency of fast neuromorphic devices, as in [329]. Particularly fast neuromorphic hardware such as Brainscales [339] might support then even more powerful offline priming with training algorithms that could not be carried out on GPU-based hardware, thereby providing the basis for superior hybrid systems.

### 16.4. Concluding remarks

Learning in neuromorphic hardware is likely to become split into two phases that each have different goals and require different learning methods: an extensive offline priming phase—either on the actual hardware or a software model for it—that optimizes selected hyperparameters but possibly also the network architecture for a large family of potential on-chip learning tasks in the hands of the user. Resulting hyperparameter values and network architectures will be downloaded onto the neuromorphic hardware before it gets into the hands of the user. The hardware is then primed so that remaining open parameters can be learnt on-chip from very few examples, possibly even just one example.

It is conceivable that this method can be expanded to provide another useful property for neuromorphic hardware in the hands of the user: that on-chip learning cannot bring the chip into an operating regime which is unsafe, or undesired for other reasons. It has already been verified that the outer loop of L2L can impose powerful priors for subsequent computing and learning of RSNNs [334].

## Acknowledgements

This research/project was supported by the Human Brain Project (Grant Agreement Number 785907) of the European Union and a Grant from Intel.
# 17. Computational neuroscience

Srikanth Ramaswamy

Newcastle University

## 17.1. Status

Understanding the brain is probably the final frontier of modern science. Rising to this challenge can provide fundamental insights into what makes us human, develop new therapeutic treatments for brain disorders, and design revolutionary information and communication tools. Recent years have witnessed phenomenal strides in employing mathematical models, theoretical analyses and computer simulations to understand the multi-scale principles governing brain function and dysfunction—a field referred to as ‘computational neuroscience’ [340, 341]. Computational neuroscience aims at distilling the necessary properties and features of a biological system across multiple spatio-temporal scales—from membrane currents, firing properties, neuronal morphology, synaptic responses, structure and function of microcircuits and brain regions, to higher-order cognitive functions such as memory, learning and behavior. Computational models enable the formulation and testing of hypotheses, which can be validated by further experiments.

The multidisciplinary foundations of computational neuroscience can be broadly attributed to neurophysiology, and the interface of experimental psychology and computer science. The first school of thought, neurophysiology, is exemplified by the model of action potential initiation and propagation proposed by Hodgkin and Huxley [342] and theoretical models of neural population dynamics [229]. Whereas, the second school of thought, at the interface of experimental psychology and computer science focuses on information processing and learning, which could be traced back to models of ANN that were developed about half a century ago [343]. Computational neuroscience became its own nascent field about three decades ago and has rapidly evolved ever since [344].

In the early stages of its conception, computational neuroscience focused almost entirely on states of sensory processing, mainly due to the fact that studies of cognitive function were restricted to the domain of psychology, which was beyond what empirical neuroscience could offer. However, since then, rapid strides in tools and techniques have enabled tremendous advances in our knowledge of the neural mechanisms brain underlying cognitive states such learning and memory, reward and decision-making, arousal and attention [345–347]. Consequently, the dynamic field of neuroscience bestows many opportunities and challenges. A recent development is the symbiosis between computational neuroscience and deep learning [313]. Deep learning models have enabled efficient means to analyze vast amounts of data to catalyze computational modeling in brain research. However, the current framework of deep learning is mostly restricted to object recognition or language translation. Identifying the fundamental mechanisms responsible for the emergence of higher cognitive functions such as attention and decision making, appropriately recapitulated into algorithms by computational models, will influence the next generation of intelligent devices.

## 17.2. Current and future challenges

Thus far, computational neuroscience has been limited to modelling local circuits—through point neurons or multicompartmental models. However, the advent of ‘big’ spatial and temporal data, from single-cell transcriptomes, micro, meso and macro-scale connectomes, large-scale neurophysiology, and functional brain activity mapping, which is nothing short of an industrial revolution in neuroscience, are rapidly enabling computational models to incorporate greater biological realism and move beyond local circuits. The grand challenge, however, is to assimilate the exponentially increasing datasets into theories and models of global brain function that complement local circuit and large-scale network models. One possibility towards surmounting this challenge is through collaborative endeavors, which have pioneered a high-throughput, team science approach to neuronal modelling. These initiatives have brought together disparate disciplines such as experimental biology, computational neuroscience, high-performance computing and data science that attempt to model neural circuits with thousands of detailed multicompartmental neurons and millions of synapses and simulate these models on supercomputers. The first of these was probably the Blue Brain Project, followed by the Allen Institute for Brain Science, the US Brain Initiative, Japan Brain and Minds, the Human Brain Project, and the International Brain Laboratory among several others [348–355].

## 17.3. Advances in science and technology to meet challenges

The brain is undoubtedly the most sophisticated information processing device known to humankind. However, the underlying principles of its function and dysfunction seem to vastly differ from those understood through conventional computing hardware. Although we are far from fully understanding the principles of
brain function, which operates by consuming a few watts of power, it still manages to solve complex problems by devising algorithms that appear to be intractable with current computing resources. The brain is robust, reliable and resilient in its operation despite the fact that its building blocks could fail. All of these are highly advantageous features to instruct the better design of the next generation of computing hardware [356].

In the future, computational models and simulations of brain function and dysfunction will be better informed through its unique capabilities to model and predict the outside environment, underlying design principles, their mechanisms and multi-scale organization and operation. The interface of experimental and computational neuroscience will shed new light on the unique biological architecture of the brain and help translate this knowledge into the development of brain-inspired technologies.

We are still at the tip of the iceberg in dealing with and solving diverse challenges. It is possible that ‘neuromorphic’ computing systems of the future will comprise billions of artificial neurons and the development, design, configuration and testing of radically different hardware systems will require new software compatible with the organizing principles of brain function. This will require a deep theoretical understanding of the way the brain implements its computational principles. Knowledge of the cognitive architectures underlying capabilities such as attention, visual and sensory perception can enable us to implement biological features that current computing systems lack.

Recent advances in ‘connectomics’ allow unprecedented reconstructions of biological brain networks. These connectomes display rich structural properties, which include heavy-tailed degree distributions, segregated ensembles and small world networks [357]. Despite these advances, how network structure determines function computation remains unknown. Going forward, approaches from computational modelling and neuroscience could better inform the design of neuromorphic systems to unravel how structure leads to function, how the same network configuration could result in a spectrum of cognitive tasks depending on the network state and how different network architectures could support the emergence of similar cognitive states.

## 17.4. Concluding remarks

The fledgling field of computational neuroscience has now transformed into a dynamic and vibrant global community. Computational models and simulations offer an unprecedented framework to understand brain function and dysfunction. But what could computational neuroscience possibly achieve in the next two or three decades? Can models of neural networks explain any higher-order brain function? For example, visual function involves progressive information processing with across brain regions with similar network architectures but different modalities of representation and computation [358]. Most neural network models to date are exclusively designed with functionally homogeneous populations of neurons, comprising simplified point neuron models or ‘ball and stick’ compartmental models that do not necessarily capture the rich features of brain organization and architecture.

Although computational neuroscience initially took off in the ‘reductionist’ direction that enabled researchers to abstract approaches and insights from disparate fields such as mathematics, physics and engineering it probably resulted in overly simplified assumptions about the unprecedented complexity of the building blocks of the brain. Future models of neural networks and theories would have to take cognizance of the astounding assortment of neuron types, impact of neuromodulatory systems, metabolic factors, co-release of neurotransmitters, neuro-glial-vasculature interactions adaptive mechanisms of neurons and synapses across diverse spatio-temporal scales among many other biological details.

In conclusion, computational neuroscientists that use their preferred level of abstraction and modeling traditions should come together as a community, which surprisingly, is rather difficult. As a famous neuroscientist and Nobel laureate, Sydney Brenner remarked ‘we are drowning in a sea of data and starving for knowledge’. Computational neuroscientists should interact a lot more with experimentalists and learn from each other. More theory is the need of the hour to crack the brain.

## Acknowledgements

S.R. acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 842492 and a Newcastle University Academic Track (NUAcT) Fellowship.
# 18. Stochastic computing

Jonathan Tapson

University of Technology, Sydney

## 18.1. Status

The human brain is extraordinarily efficient in computation, using at least five orders of magnitude less power than the best neuromorphic silicon circuits [359]. Nonetheless, it still consumes approximately 20%–25% of a human’s available metabolic energy, and it is safe to assume that the evolutionary pressure to optimize for power efficiency in the brain was extremely severe [360]. It therefore comes as a suprise that the transmission of signals through the brain’s synaptic junctions is apparently noisy and inefficient, with probabilities of 0.4–0.8 for transmission of an axonal spike being typical (see [361] for a detailed review). This raises the question: is this transmission variability a bug or a feature? Also, can any brain-inspired computational system which does not include synaptic stochasticity capture the essence of human thought? Perhaps it serves to regularize biological neural networks, in the same way that machine learning techniques such as dropout are used to make ANN more robust (figure 25).

This, and many other similar questions, drive the field of stochastic computation [362, 363]. The field covers a large number of techniques in which some kind of probabilistic function, filter or network is used to create a computational output which would not be possible with deterministic systems. For example, in neuromorphic neural networks, the use of nonlinear random projections has become a commonplace method for raising the dimensionality of an input space prior to a learned solution layer. Technologies as diverse as silicon device mismatch, memristor mismatch, and even random networks of conductive fibres have been proposed and tested for this purpose [364]. Generally, stochastic computation methodologies fall into a number of categories:

(a) Systems where noise or randomness is used to add energy to a system, enabling it to traverse or establish states which were otherwise inaccessible. Energy in this sense means potential or kinetic energy (in mechanical, electrical or chemical form), rather than general system power consumption. The various phenomena of stochastic resonance and stochastic facilitation [362] are typical examples of these systems.

(b) Systems where the data or input streams are intrinsically random or noisy, and rather than filter or otherwise reduce the uncertainty in the signals, a computational system is devised which processes the raw signal to produce a computationally optimal output. Recently, many of these systems apply Bayesian models [365], particularly when derived from biological principles.

(c) Systems in which it is required to project the input space nonlinearly to a higher dimension, in order to facilitate linear or other numerical solutions to some regression or classification problem. This obviously includes conventional neural networks; however, there is an increasing body of research in both human neuroscience and machine learning in which random nonlinear projections are found to be optimal for some function.

## 18.2. Current and future challenges

Perhaps the most exciting theme for the future of stochastic computation is the modelling of random or noisy processes in the brain, and applying these to real world computational problems. For example, there is increasing evidence that random projection layers are used in the brain to improve the versatility and selectivity of neural circuits [366]. This has parallels in random projection ANN, but the utility of these systems has, until now, been overshadowed by the performance of application specific machine learning systems. Additionally, there is evidence that in SNN, noise appears to enable probabilistic inference [367]. As these systems move from academia into commercial fields of exploitation, the adaptability and robustness of the random networks is likely to shine.

There are some significant issues in exploiting stochastic computation. For example, if randomness is created using intrinsically variable hardware means such as device mismatch, then the randomness is unique to each device, and therefore each device will need to be characterised before it can be used. This is reasonable in laboratory research, but infeasible as part of the workflow in producing highvolume consumer goods.

A further problem for stochastic computation is that many applications—for example, biomedical or aerospace systems—require deterministic and provably failsafe operation. Systems that compute based on probability and statistical likelihoods may struggle to meet industrial standards, notwithstanding that their performance may be superior to current methods.
<figure><img src="image_28.png" /><figcaption>**Figure 25.** Illustration of a simple stochastic computation, using logic gates (AND or MUX) to compute on strings of bits representing probabilistic coding of numbers. Note that single-bit errors in the computation (as a result of noise) will not significantly change the output. After [362].</figcaption></figure>

## 18.3. Advances in science and technology to meet challenges

The foundations of stochastic computation are not mature. The field at present consists largely of proofs-of-concept, with few widely-applicable principles that can be applied in a generic way. Noise and randomness are still seen as obstacles to be overcome in signal processing, so unless a practitioner has some prior experience of stochastic computing, there is little likelihood of applying these methods as part of a standard process. Nonetheless, the increasing interest in Bayesian methods, and their existence and application in biological neural systems, is generating growing interest in this approach. What the field requires for advancement are the following:

* A foundational theory of probabilistic computing, integrating Bayesian, SR and random projections into a more general framework which answers the question: 'I have noisy and nonlinear input data, or a noisy and nonlinear system. How do I take advantage of the energy and nonlinearity rather than simply filtering it out?'

* Current methods of implementing random connective weights and random numbers in silicon are not ideal. In particular, there are few methods which allow the deterministic generation of 'random' weights in ways that obviate the requirement to characterise each individual part prior to computation. Similarly, generation of random numbers 'on the fly' in computational platforms such as custom silicon or FPGAs requires a significant proportion of hardware and energy to be dedicated to it.

## 18.4. Concluding remarks

The real world and natural organisms are intrinsically noisy and variable, and yet the most efficient computer known—the human brain—computes very successfully using noisy and variable hardware on noisy and variable signals. As our understanding of this phenomenon grows, the desire to apply the principles in artificial computation will grow accordingly. In stochastic computation, we have the beginnings of a model for this process, and it seems likely that in the future we will regard computational systems which do not use probabilistic principles as being as limited as we now regard classification and regression using the purely analytic methods that were commonplace before machine learning.
# 19. Convolutional spiking neural networks

Priyadarshini Panda and Youngeun Kim

Yale University, United States of America

## 19.1. Current status

Recent machine learning literature show that convolutional neural networks have been adopted successfully in various vision applications [5, 368]. To apply SNNs to diverse vision tasks, convolutional SNNs (figure 26) should be explored as a priority. Consequently, there have been many attempts to train a convolutional SNN architecture. Conversion [373] proposes a weight balancing technique in order to convert pre-trained convolutional ANNs to convolutional SNNs. Note, we make a distinction between convolutional ANN and convolutional SNN based on the type of input processing and the neuronal activation as shown in figure 26. The overall architecture still uses convolution and pooling operation suitable for enhanced performance in vision tasks. The conversion method yields competitive accuracy, but requires careful weight scaling to emulate the floating point activation value of ANNs and therefore its application is restricted to simple static image recognition. Surrogate gradient learning circumvents the non-differentiability of a LIF neuron by defining an approximate gradient during backward propagation [374]. Training SNNs directly on spike trains can enable parsing various types of input representation beyond static images, and potentially lead to SNN implementation for diverse computer vision applications.

Further, the development of convolutional SNNs is in tandem with a growing interest in dynamic vision sensors (DVS). Different from a standard frame-based camera, a bio-plausible DVS camera emulates the human retina and generates asynchronous and binary spikes according to illumination changes. Therefore, this visual sensor has the advantage of a high frame rate, low energy consumption, and less blurring effect compared to a conventional frame-based camera [375]. A convolutional ANN fails to exploit the advantages of DVS camera inputs since ReLU neurons cannot capture temporal information. On the other hand, owing to temporal spiking neuronal dynamics, convolutional SNNs are a natural fit for processing DVS data. Also, combining DVS camera inputs with SNNs yields a fully-spiking and energy-efficient system. Despite the potential advantages, an algorithm for training convolutional SNNs on DVS data is still underdeveloped.

## 19.2. Challenges

The majority of previous works on convolutional SNNs classify static images. SNNs require a spike train to process in the temporal domain. Thus, various coding schemes have been proposed for converting static images to spikes [386]. It is important to select a proper coding strategy since the energy consumption on an asynchronous neuromorphic hardware is approximately proportional to the number of spikes. Currently, rate coding is the most widely-used coding scheme since it yields high application-level accuracy. But, rate coding generates spikes, where the number of spikes is proportional to the pixel intensity. This causes multiple (and sometimes redundant) spikes per neuron and therefore reduces the energy-efficiency of the overall system. In order to bring more energy advantages, a new coding scheme with fewer spikes should be explored.

Another challenge is directly training deep convolutional SNNs. From ANN literature, it is well known that network depth is a crucial factor for achieving high accuracy on vision tasks. ANN-toSNN conversion enables deep SNNs with competitive accuracy. But, emulating float activation with multiple binary spikes requires a large number of time-steps, which in turn increases overall energy and latency. Surrogate gradient learning allows short latency and can be used with flexible input representations, but it suffers convergence issues when we scale up the depth. Therefore, convolutional SNNs with surrogate learning are still restricted to shallow networks on trivial datasets. Overall, effective spike-based training techniques for deep convolutional SNNs is necessary to reap the full energy-efficiency advantages of SNNs.

Finally, there is a need to investigate SNNs beyond the perspective of accuracy and energy efficiency. Good-fellow et al [376] showed that unrecognizable noise can induce a significant accuracy drop in ANNs. This questions the reliability of ANNs since humans do not misclassify when presented with such perturbed adversarial inputs. In this light, there is a need to analyze the robustness of SNNs. Furthermore, the internal spike behavior of SNNs still remains to be a 'blackbox' as that of conventional ANNs. In the ANN domain, several interpretation tools have been proposed and provide cues for advanced computer vision applications such as visual-question answering. In a similar vein, an SNN interpretation tool should be explored because of its potential usage for real-world applications where interpretability in addition to high energy-efficiency is crucial.
<figure><img src="image_29.png" /></figure>

19.3. Advances in algorithms and hardware to meet challenges

Beyond rate coding, temporal coding generates one spike per one neuron in which spike latency is inversely proportional to the pixel intensity. Mostafa et al [377] applied an exponential kernel to derive locally exact gradients of spiking neurons. The authors trains SNNs on XOR problem and MNIST dataset with much fewer number of spikes compared to standard rate coding. Han and Roy [378] tackled the memory access issue for tracking a synapse at every time-step until the synapse receives its first spike. They use two signed spikes that denote the start and finish time-step respectively, improving energy-efficiency. Besides temporal coding, phase coding has also been proposed based on biological observation [379], which encodes temporal information into spike patterns based on a global oscillator in an energy-efficient manner [380].

In order to address the shortcomings of ANN-to-SNN conversion and surrogate gradient learning, Rathi et al [381] proposed hybrid training-a conversion process followed by surrogate gradient learning. As a result, they achieve competitive performance with significantly lower latency. Wu et al [382] presented a spatio-temporal backpropagation technique for direct training convolutional SNNs. The authors evaluate their method on static MNIST as well as DVS-based N-MNIST. Recently, Kim and Panda [383] addressed the scalability problem of direct spike-based training by proposing a time-specific batch normalization technique, called batch normalization through time (BNTT). The authors successfully trained convolutional SNNs from scratch with BNTT spike-based training on complicated datasets such as CIFAR100, Tiny-ImageNet and DVS CIFAR10.

Few recent studies have shown that SNNs have more robustness compared to their ANN counterparts. For the first time, Sharmin et al [384] observed that SNNs are more robust with respect to adversarial images. They provided a comprehensive analysis on VGG9 networks trained on CIFAR10 dataset with various attack scenarios including black-box and white-box attacks. Also, the authors of [385] asserted that a Poisson coding generator and non-differentiable neuronal dynamics are the main reasons for SNN robustness. Recently, the authors of [370] presented a heatmap visualization tool that shows where SNN focuses on during classification. The results support that SNNs show more robust attention on discriminative regions with respect to adversarial samples. We believe that research on interpretability is essential to open up the possibility of an interpretable and reliable neuromorphic system.

Finally, from a hardware implementation perspective, memristive crossbar array based neural network accelerators have been shown to be area and energy efficient by many orders of magnitude than fully digital CMOS implementations [386]. Thus, it is natural to explore the possibility of implementing convolutional SNNs on such crossbars. But, the convolutional architecture requires a careful weight mapping process. This is because the matrix-vector-multiplication operation of the crossbar arrays are suitable for a fully-connected layer. On the other hand, a convolutional layer uses spatially shared weight kernel where in, same weight kernel interact with different input patches to produce different outputs. Thus, weight sharing can lead to ineffective
crossbar utilization (see [386] for more details). To address the mapping problem due to the weight sharing, the authors in [371, 372] present a mapping protocol for convolutional ANNs. They also provide a simulation tool for crossbar implementation, which evaluates the energy and performance of a network during inference on crossbars. We believe that similar mapping protocols can be extended to convolutional SNNs.

### 19.4. Concluding remarks

Convolutional architecture plays a key role in AI. Therefore, this architecture is also important in the neuro-morphic community because of its practical usage for expanding SNN applicability for diverse vision scenarios. Moreover, from a neuroscientific perspective, convolutional architecture is an interesting research topic itself. For example, Hubel and Wiesel [368] discovered a hierarchical model like convolutional architectures in the primary visual cortex (V1) of cats. After two decades, Fukushima and Miyake [369] suggested a more struc-tured neural system, which consists of S-cells extracting visual features and C-cells working as a non-linear function (like ReLU). Today, convolutional ANNs have found use in neuroscience studies to interpret the activ-ity recorded from different brain regions and explain about the functionality of the region. Since convolutional SNNs are more bio-plausible given the temporal spike processing, we believe convolutional SNNs alongside suitable neuromorphic hardware can be used as interpretation tools for neuroscience. Finally, in order to bring the convolutional architecture to a neuromorphic system, recent works have focused on energy-efficient coding schemes and training techniques. In addition to the development of SNNs with higher energy-efficiency and accuracy, robustness and interpretability of convolutional SNNs are significant research directions. Overall, many technical challenges, such as training, coding, and interpretability need to be addressed to build reliable and accurate convolutional SNNs compatible with neuromorphic hardware.

### Acknowledgements

The research was funded in part by C-BRIC, one of six centres in JUMP, a Semiconductor Research Corporation (SRC) program sponsored by DARPA, the National Science Foundation, the Technology Innovation Institute (Abu Dhabi) and the Amazon Research Award.
# 20. Reservoir computing

Gouhei Tanaka

The University of Tokyo

## 20.1. Status

Reservoir computing (RC) is a machine learning framework capable of fast learning, suited mainly for temporal/sequential information processing [387]. The general concept of RC is to transform sequential input data into a high-dimensional dynamical state using a 'reservoir' and then perform a pattern analysis for the reservoir state in a 'readout'. This concept was originally conceived with a special class of recurrent neural network (RNN) models (see figure 27(a)), such as echo state networks (ESNs) [388] and liquid state machines (LSMs) [389]. The main characteristic is that the reservoir is fixed and only the readout is adapted or optimized using a simple (mostly linear) learning algorithm, thereby enabling fast model training. Owing to the computational efficiency, software-based RC on general-purpose digital computers has been widely applied to pattern recognition, such as classification, prediction, system control, and anomaly detection, for various time series data. To improve computational performance, many variants of RC models have been actively studied [390].

On the other hand, hardware-based RC is an attracting option for realizing efficient machine learning devices. A reservoir can be constructed not only with RNNs but also with other nonlinear systems. In fact, a rich variety of physical reservoirs have been demonstrated using electrical, photonic, spintronic, mechanical, material, biological, and many other systems (see figure 27(b)) [391]. Such physical RC is promising for developing novel machine learning devices as well as for finding unconventional physical substrates available for computation. The system architectures of hardware-based reservoir can be mainly classified into several types, including network-type reservoirs consisting of nonlinear nodes, single-nonlinear-node reservoirs with time-delayed feedback [392], and continuous medium reservoirs [393]. Many efforts are currently underway to improve computational performance, enhance energy efficiency, reduce computational cost, and promote implementation efficiency of the physical reservoirs. They are often combined with a software-based readout or a readout device based on reconfigurable hardware capable of multiply-accumulate operation.

Further advances in physical RC would contribute to realizing novel AI chips, which are distinguished from AI chips for deep learning. One of their potential targets is edge computing [394]. High-speed machine learning computation for data stream obtained from sensors and terminal devices would lead to data traffic reduction and data security enhancement in the Internet of Things (IoT) society.

## 20.2. Current and future challenges

In exchange for the merit that speedy training is possible for RC systems with simple readouts, the fixed reservoir needs to be well designed for high computational performance. For ESNs and LSMs, there are plenty of theoretical and experimental results helpful in designing 'good' RNN-based reservoirs [395-398]. So far, the potential of RC framework has been shown for many benchmark tasks and practical applications (see table 3) [391]. Current challenges on software-based RC include enhancing the utility of RC models for industrial applications, comparing their cost-performance ratio with that of other machine learning methods, and developing extremely efficient RC models through various extensions.

For hardware-based RC, however, design principles have yet to be fully established. Therefore, a fundamental challenge is to make recipes for constructing 'good' physical reservoirs as well as identifying the main components governing the computational performance. The computational performance of RC systems relies on dynamical states of reservoirs when driven by input signals, which should have nonlinearity and memory (or history dependency) for temporal pattern recognition. The response characteristics of reservoirs can differ depending on the type of reservoir architecture, the physical property of the media/substrates in the reservoir, and the pre- and post-processing of signals to and from the reservoir [391, 399]. It is not straightforward to clarify how each of these factors influences the computational ability of the whole RC system, because they are not independent of each other in many cases [400]. In hardware implementation of physical reservoirs, it is needed to consider physical constraints, such as possible time resolution and measurement precision in signal processing, which could also affect the computational property [401]. Therefore, more numerical and experimental studies are necessary for elucidating the relationship between physical and computational properties of RC systems [402].

Toward practical applications of hardware-based RC systems, it is demanded to evaluate each physical reservoir in terms of different aspects such as computational performance, processing speed, power efficiency, scalability, miniaturizability, and robustness. After an accumulation of these evaluations for various pattern recognition tasks, it would be possible to make a comparison between different RC systems and determine
<figure><img src="image_30.png" /><figcaption>**Table 3.** Examples of subjects in RC applications. Table reproduced from (Tanaka et al 2019). CC BY 4.0.</figcaption></figure>

<table><thead><tr><th>Category</th><th>Examples</th></tr></thead><tbody><tr><td>Biomedical</td><td>EEG, fMRI, ECG, EMG, heart rates, biomarkers, BMI, eye movement, mammogram, lung images</td></tr><tr><td>Visual</td><td>Images, videos</td></tr><tr><td>Audio</td><td>Speech, sounds, music, bird calls</td></tr><tr><td>Machinery</td><td>Vehicles, robots, sensors, motors, compressors, controllers, actuators</td></tr><tr><td>Engineering</td><td>Power plants, power lines, renewable energy, engines, fuel cells, batteries, gas flows, diesel oil, coal mines, hydraulic excavators, steam generators, roller mills, footbridges, air conditioners</td></tr><tr><td>Communication</td><td>Radio waves, telephone calls, internet traffic</td></tr><tr><td>Environmental</td><td>Wind power and speed, ozone concentration, PM2.5, wastewater, rainfall, seismicity</td></tr><tr><td>Security</td><td>Cryptography</td></tr><tr><td>Financial</td><td>Stock price, stock index, exchange rate</td></tr><tr><td>Social</td><td>Language, grammar, syntax, smart phone</td></tr></tbody></table>

which physical RC system meets a specific purpose. It is also significant to promote an integration of RC-based machine learning devices with IoT devices.

## 20.3. Advances in science and technology to meet challenges

RC models can be more efficient by using additional techniques and extended architectures. For instance, a hybrid of an ESN and a knowledge-based model enables better prediction of spatiotemporal chaotic behaviour in high-dimensional dynamical systems [403]. RC models can also be combined with some other machine learning techniques found in this roadmap. Another extended RC model is the deep RC model consisting of multiple reservoirs [404], which is effective for diversifying temporal representations and generating rich dynamical behaviour.

Methodologies for designing and implementing hardware-based RC systems are currently under intensive investigation. For instance, substantial progress has been made for electronic RC [405] and photonic RC [406]. Compared with them, other physical RC systems are still in the initial stage of development [391, 399]. More studies are required to harness nonlinearity and memory of physical reservoirs for efficient computation.

On the one hand, it is significant to improve computational performance of physically implemented RC hardware such that it is competitive with other machine learning hardware. On the other hand, mathematical modelling and simulations of physical reservoirs are useful for proof-of-concept of novel hardware-based RC systems and analyses of their mechanisms. Both approaches are complementary for addressing the challenges mentioned above.

SNNs, which is one of the central topics in neuromorphic computing, have often been used for implementing a reservoir, mostly in the context of LSMs. Neuromorphic chips based on SNNs (e.g. BrainScaleS 2 prototype system) are available for emulating LSM models and thereby useful for exploring optimal setting of SNN-based reservoirs [407]. In the future, efficient SNN-based machine learning hardware for real-time computation could be realized based on the RC framework with biologically plausible mechanisms.

## 20.4. Concluding remarks

Toward realizing extremely efficient RC systems and hardware for temporal pattern recognition, both algorithmic and implementation efforts are necessary [408]. Moreover, both physical and computational viewpoints
are required for development of physical RC. Therefore, the progress of physical RC would be accelerated by
interdisciplinary collaborations between experts in different research areas.

Acknowledgements

This work was partially based on results obtained from a project, JPNP16007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO), and supported in part by JSPS KAK-ENHI Grant Number 20K11882, JST CREST Grant Number JPMJCR19K2, and JST-Mirai Program Grant Number JPMJMI19B1.
# 21. Computing with spikes

Simon Thorpe

CerCo-CNRS

## 21.1. Status

Deep learning architectures now dominate AI. But although they are superficially neurally-inspired, there are significant differences with biology. The ‘neurons’ in such systems typically send floating-point numbers, whereas real neurons send spikes. Attempting to model a system with the complexity of the human brain with floating-point numbers seems doomed to failure. The brain has 86 billion neurons, with around 7000 synapses each on average. Real-time simulation of such a system with a resolution of 1 millisecond would require (8.6 × 10⁷) × (7.0 × 10³) × (1.0 × 10³) floating-point operations a second—over 600 PetaFLOPS, even without worrying about the details of individual neurons. This would saturate the most powerful supercomputer on the planet and require 30 Megawatts of power—over one million times the brain’s remarkable 20 W budget. How does the brain achieve such a low energy budget? It seems very likely that spikes could be a key to this efficiency and a reason why, since the late 1990s, SNN have attracted increasing interest [6, 409, 410].

A first critical advantage is that computation only occurs when there are spikes to process. The AER protocol (address event representation), first proposed in 1992 [411], communicates by sending lists of spikes. It is used in many neuromorphic systems, including DVS (see section 21) and the multi-million processor SpiNNaker project [203, 412]. An early event-driven spiking neuron simulator was the original version of SpikeNet [413, 414]. At the time, the joke was that such a system could simulate the entire human brain in real-time—as long as none of the neurons spiked!

Second, spikes allow the development of far more efficient coding schemes. Researchers in both neuroscience and neural networks typically assume that neurons send information using a firing rate code. And yet, the very first recording of responses of the optic nerve by Lord Adrian in Cambridge in the 1920s demonstrated that while increasing the luminosity of a flashed stimuli increased both the peak firing rate and maintained firing rate of fibres, there was also a striking reduction in latency [415]. Thus, even with a flashed stimulus, response latency is not fixed. Sensory neurons effectively act as intensity-to-delay convertors—a fact effectively ignored for over six decades. But in 1990, it was proposed that spike-arrival times across a population of neurons could be a highly efficient code [416], an idea confirmed experimentally for the retina in 2008 [417].

## 21.2. Coding with spiking neurons

If we accept that sensory neurons can act as intensity-to-latency convertors, it opens a whole range of interesting options. Figure 28 compares various spike coding schemes for transmitting the activation levels for 16 input channels labelled A-P with 4 bit resolution. The ‘standard’ method would be rate coding, which counts the number of spikes in a fixed period (e.g. 100 ms). But there are numerous drawbacks of such an approach. First, how do you determine the length of the observation window? Second, rate coding is exceptionally vulnerable to changes in global intensity or contrast of the input profile that completely disrupt firing rates.

Using a temporal coding scheme avoids these problems. One option uses the order of firing as a code—rank order coding [418]. Synaptic weights are set to values that depend on the input’s rank. Then a simple feed-forward shunting inhibition mechanism makes neurons selective by progressively reducing the effectiveness of later spikes. Although feasible, rank order coding is perhaps too powerful. Even with just 16 inputs, there are factorial 16 possible firing orders—nearly 21 trillion. Each pattern could be detected by a neuron with the appropriate set of weights.

Another option, N-of-M coding [419], is much simpler. It uses a feedback inhibition circuit to count input spikes and block further firing once a given number of inputs have fired—an inexpensive way to implement a k-WTA (Winner-take-all circuit). If a target neuron has W connections, it can detect specific combinations of inputs. Effectively, each neuron gets a level of excitation that depends on how well the N inputs spikes match their set of weights. Crucially, this can be done with binary synapses, greatly simplifying the implementation.

Interestingly, this sort of coding had already been implemented in a commercial image processing package developed by SpikeNet Technology around two decades ago. The one published paper on the technology [420] explained that synapses between the eight orientation maps and neurons in the recognition maps were very sparse (less than 1% of possible connections). But it failed to mention that all the connections were binary—simply because the company wanted to keep its ‘secret sauce’ secret!

## 21.3. Learning with spiking neurons

Switching to temporal coding with spiking neurons also makes it possible to envisage much more efficient learning algorithms. Standard backpropagation can undoubtedly train systems to solve particular problems
<figure><img src="image_31.png" /><figcaption>**Figure 28.** Comparison of three different spike-based coding strategies. **Top:** conventional rate coding using counts of spikes in an relatively long observation window. **Middle:** rank order coding uses the order of firing of just the first spike in a shorter window. **Bottom:** N of M coding which limits the number of spikes that a transmitted allows very rapid and efficient transmission of large amounts of data.</figcaption></figure>

but requires huge numbers of training trials with labelled data. Fortunately, a human infant's brain does not need to be trained with millions of images of dogs and cats to categorize new images correctly! Instead, they can learn about new objects very rapidly. There is now good evidence that humans learn to detect virtually anything new that repeats, with no need for labelled data. If humans listen to meaningless Gaussian noise containing sections that repeat, they rapidly notice the repeating structure and form memories lasting for weeks [421]. And in experiments where random images from the ImageNet database are flashed at rates of up to 120 frames per second, humans notice images that repeat, even with only 2–5 presentations [422]. None of the existing floating-point (or rate-based) supervised learning schemes could explain such learning. In contrast, a simple spike-time dependent plasticity rule that reinforces synapses activated before the target neuron fires makes them develop selectivity to patterns of input spikes that repeat [423], and will even find the start of the pattern [424]. Similar methods have also been used to generate selectivity to repeating patterns in the output of a dynamic vision sensor corresponding to cars going by on a freeway—again in a totally unsupervised way [425].

These STDP based learning rules use continuously variable synaptic weights and typically require tens of repeats to find the repeating pattern, even when all parameters are optimized. But we have recently developed a new learning rule called JAST using binary weights [426] that can match our ability to spot repeating patterns
in as few as 2–5 presentations. The target neuron starts with a fixed number of binary connections. Then, instead of varying the strength of the synapses (as in conventional STDP learning), the algorithm effectively swaps the locations of the connections to match the repeating input pattern.

The algorithm was originally implemented on a low-cost Spartan-6 FPGA, already capable of implementing a network with 4096 inputs and 1024 output neurons, and calculating the activation level and updating all the outputs 100 000 times a second. The circuit also included the learning algorithm on-chip.

## 21.4. Concluding remarks

Although the deep learning revolution has transformed the landscape and allowed the development of artificial systems that rival or surpass human levels of performance, current systems lag behind their biological equivalents on several fronts. They are several orders of magnitude less energy efficient than biological systems, and the mode of learning is highly non-biological.

SNN could overcome many of these limitations in the next few years. Firstly, current systems are handi-capped by very inefficient rate-based coding schemes or their floating-point equivalents. Spikes allow the use of temporal coding schemes such as rank order coding and N-of-M coding that are way more efficient. Second, spikes allow the use of spike-time dependent plasticity rules, some of which operate with binary connections and can learn to recognize repeating patterns very efficiently. Such coding schemes avoid the internal noise problems associated with Poisson rate coding, since they are effectively completely deterministic. Of course, there will always be some degree of noise in the sensory data and in the mechanism leading to spike initiation in the sensors themselves.

Finally, sending information as spikes will allow the implementation of vast networks with billions of neurons and trillions of connections even with currently available technology. The critical question is the level of sparseness that can be achieved. It is quite possible that many cortical neurons are silent a lot of the time ('neocortical dark matter'), and that each neuron may only need a small number of (binary) connections. If so, the future impact of spike-based computation could be enormous.

## Acknowledgements

The author thanks the CNRS for support.
# Section 4. Applications

## 22. Robotics

Chiara Bartolozzi

Istituto Italiano di Tecnologia (IIT)

### 22.1. Status

Neuromorphic systems, being inspired by how the brain computes, are a key technology for the implementation of artificial systems that solve problems that the brain solves, under very similar constraints and challenges. As such, they hold the promise to efficiently implement autonomous systems capable of robustly understanding the external world in relation to themselves, and plan and execute appropriate actions.

The first neuromorphic robots were proof of concepts based on ad hoc hardware devices that emulated biological motion perception [427]. They relied on the know-how of chip designers, who had to manually turn knobs to tune the chip behaviour. That seed could grow into a mature field thanks to the availability of hardware that could be more easily tuned by nonexperts with standard software tools [428] and of quality DVS [429] and neuromorphic computing chips and systems [412, 430] featuring many instances of neurons and (learning) synapses that could be used as computational primitives for perception and decision making. Since then, neuromorphic robotics followed three main paths, with the development of visual perception for robots using event-driven (dynamic) vision sensors [375, 431], proof-of-concept systems linking sensing to control [432] and SNN for the control of motors [433, 434]. At the same time, the neurorobotics community started developing models of perception, cognition and behaviour based on SNN, with recent attempts to implement those on neuromorphic platforms [435–437]. Finally, the computational neuroscience community has developed learning theories to reconcile DNNs with biologically inspired spike-based learning and to directly develop spiking neural models for motor control that in the future could be implemented on neuromorphic hardware [438–440].

In this rich and lively scenario, the multiple contributing communities and research fields have the potential to lead to the next breakthrough, whereby neuromorphic sensing and computing support the development of smart, efficient and robust robots. This research is timely and necessary, as robots are moving from extremely controlled environments, to spaces where they collaborate with humans, where they must dynamically adapt, borrowing from neural computational principles (figure 29).

### 22.2. Current and future challenges

The complexity of robotics poses a number of challenges for the neuromorphic community, that needs to demonstrate scalable methods for sensing, perception, decision making and control, that can be deployed in complex applications.

The event-driven readout should be applied to the majority of sensors on the platforms, including those that convey information about the robot's state (inertial, F/T sensors, encoders, etc). Additionally, more sophisticated event-driven encoding could be implemented, by using local computation that emulates the neural encoding of specific sensory modalities.

Understanding the neural code relative to stimuli properties up to decision making [441] will guide the design of SNN to extract meaningful information and take appropriate decisions.

Controlling a robot requires translating information from the sensory to the joint domain, coordinating different joints to implement stable and smooth trajectories, and counteracting actuation errors. Also in this domain, it is crucial to understand principles of biological control and actuation that lead from the cortical motor commands, down to muscles' recruitment and the relative feedback signals.

At the same time, the big question is how much these principles are shaped on the properties of muscles, and how they have to change to be applied to any type of physical (artificial) actuation. Hidden in this research domain is the intrinsic link between the neural code for actuation and the sensory feedback code that interacts with control signals to effectively drive the system.

Given the number of components that need to be developed and orchestrated, a possible path is the progressive integration of specific neuromorphic modules within a 'traditional' robotic architecture. However, the overall system's architectures might be based on very different assumptions and this hybrid architecture might not fully exploit the advantages of the neuromorphic paradigm. A fully neuromorphic system should be the final goal, requiring an exponential growth of the community working on all the bits and pieces that compose a robotic system.
<figure><img src="image_32.png" /><figcaption>**Figure 29.** Neuromorphic robots: on the left the tracker chip mounted on a pan-tilt unit [427], on the right the iCub humanoid platform featuring event-driven vision sensors.</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>**Figure 30.** Timeline of a possible development roadmap. In green required theoretical advancements in order of increasing complexity. In red the technological roadmap highlighting the path for new circuit and devices development, as well as the infrastructure needed for the integration on robotic platforms.</figcaption></figure>

The signature of neuromorphic robots will be continuous learning and adaptation to different environments, different tasks, changes in the robot plant, different collaborators. This must be supported by hardware capable of handling plasticity at multiple temporal scales and a strong knowledge of how the brain implements such mechanisms.

At the technological level, it is paramount to develop neuromorphic devices that can be embedded on robots, increasing the neurons and synapses count and fan-in fan-out capabilities, while maintaining a low power budget. Ideally, those devices have standard interfaces that do not require the use of additional components to be connected to the software infrastructure of the robots. With growing task complexity, and the need of multiple hardware platforms to run different computational modules, the neuromorphic paradigm could take advantage of robotic middlewares, such as ROS, or YARP, that are currently seamlessly integrated with neuromorphic sensors and computing devices.

### 22.3. Advances in science and technology to meet challenges

The main components in which robotics is traditionally categorised—perception, decisionmaking and control—are strictly connected and influence each other. Studying their interplay and understanding how the neural code reflects sensory information, decision processes and how these are influenced by action and vice versa, is the future research challenge to bring neuromorphic agents to a level of complexity capable of enabling robots to effectively interact with humans.
An advancement in the understanding of the role of different brain areas, their working principles and their interaction with other areas across different temporal and spatial scales shall guide the design of artificial architectures using spiking neuron models, synaptic plasticity mechanisms, connectivity structures to implement specific functionalities. It is crucial to find the right level of detail and abstraction of each neural computational primitive and develop a principled methodology to combine them. Starting from highly detailed models of brain areas, the community shall find reduced models that can implement their basic functionality and that can be implementable on neuromorphic hardware.

As the community is now developing SNN to extract information from a single sensory modality, the next step would be to take into account information from other sensory modalities, so that decisions depend on the state of the environment, of the robot and of the ongoing task. Among many others, a key area to take inspiration from is the cerebellum, that supports the acquisition of motor plans and their adaptation to the current (sensed) conditions [442]. The resulting computational frameworks shall therefore include dynamic and continuous learning and adaptation.

On the other hand, progress is necessary in the neuromorphic hardware supporting those new frameworks. New circuits for the emulation of additional computational primitives are needed, as well as the possibility to support dynamic, continuous learning and adaptation at multiple timescales.

Specific to the robotic domain, neuromorphic devices should be truly embeddable. To this aim, standardisation of communication protocols, programming tools, online integration with the robot's middleware must be developed. The necessary miniaturisation to pack more computational resources on a single system that can be mounted on a robot goes through the integration of silicon devices with memristive devices. On a longer term, nanotechnology and flexible electronics could represent a viable solution to further miniaturize, or distribute computational substrates that can de-localise computation to the periphery, or create folded structures similar to the cortex, that through folding increased the surface available for computation, achieving higher computational capabilities (figure 30).

## 22.4. Concluding remarks

Neuromorphic sensing, computing and actuation are based on design principles that will make them excel in applications for which animals have evolved. At the same time, animals' behaviour does not only depend on the brain but also on its embodiment, as the shape and distribution of sensors and of muscles, tendons, bones and their interaction influence how the sensory signals are acquired and must be interpreted, and how the control signal has to be delivered. The continuous interplay between brain, body and environment shapes neural computation and this must be taken into account when tailoring neuromorphic computation to robots.

The result of this research path will be crucial and timely for developing the next generation of robots that face the extremely hard challenge of collaborating with humans in human-designed habitat.

The interdisciplinary nature of the neuromorphic approach results in a call for action towards different research communities and different souls within the neuromorphic community. Computational neuroscience and machine learning are called to be the theoretical backbone of neuromorphic computing, micro- and nano-electronics, engineering, physics and material science are called to develop the next generation physical substrate for neuromorphic sensing and computing.

## Acknowledgements

The author would like to thank E Donati for fun and insightful discussions and brainstorming on the topic.
## 23. Self-driving cars

Jonathan Tapson

University of Technology, Sydney

### 23.1. Status

Self-driving cars have been a staple of science fiction for decades; more recently, they have seemed like an attainable goal in the near future. The machine-learning (ML) boom of the period 2015–2020 gave great cause for optimism, with experts such as the US Secretary of Transport Anthony Foxx declaring in 2016 [443] that ‘By 2021, we will see autonomous vehicles in operation across the country in ways that we [only] imagine today…My daughter, who will be 16 in 2021, will not have her driver’s license. She will be using a service’.

This optimism has faded away in the last three years, with the recognition that while it is straightforward to make cars autonomous in simple environments such as freeway driving, there are a multitude of situations where driving becomes too complex for current solutions to achieve autonomy. It is tempting to refer to these as ‘corner cases’ or ‘edge cases’—in the sense of being a highly unlikely combination of circumstances, at a ‘corner’ or ‘edge’ of the feature space, which produces a situation where a machine learning algorithms fails to operate correctly—except that, in the real-world of driving, these situations appear to be far more common than was originally expected.

It may be helpful to use the industry terminology when discussing self-driving cars. Self-driving is more formally known as Advanced Driver Assistance Systems (ADAS) and the industry generally uses the Society for Automotive Engineering’s (SAE) five-level ADAS model, illustrated below, when discussing autonomous driving capabilities (figure 31).

The more recent perception of ADAS progress can be summed up in a quote from Prof Mary Cummings, Director of Duke University’s Humans and Autonomy Laboratory [444]: ‘there are basically two camps. First are those who understand that full autonomy is not really achievable on any large scale, but are pretending they are still in the game to keep investors happy. Second are those who are in denial and really believe it is going to happen’.

Between the optimistic and pessimistic extremes, there is a consensus view amongst ADAS researchers that while full level 5 ADAS is unlikely to be available in the next five years, level 4 ADAS is both an attainable and useful target.

### 23.2. Current and future challenges

One core challenge in achieving high levels of ADAS is perception, and particularly visual perception. It has become apparent that the visual perception of human drivers is extraordinarily hard to reproduce artificially. The problem may be summed up with the following example: most, if not all, human drivers can infer the intent of a pedestrian viewed at a distance of 100 m. This is critically important for driving at moderate to high speeds (70–100 km h⁻¹) in non-freeway environments, and is as yet impossible to achieve with machine vision [445]. Consider that a pedestrian 1.5 m high when viewed at 100 m distance subtends an angle of less than 1° vertically and perhaps 0.1° horizontally. When imaged by a video imager through a moderately wide-angle lens, such as is necessary for the forward-facing cameras in an ADAS system, this corresponds to about 3 × 16 pixels in an HD system. It might be thought that the system is improved by increasing the image resolution (it would be 10 × 85 pixels in a 4 K system) but this also increases the space which must be searched for the pixels of interest.

A second core challenge in ADAS is the complexity of the world that must be modeled by the machine learning systems. Consider the example of a driver who sees a soccer ball bounce across the road in front of their moving car. A human driver is able to draw on an entire world experience concerning balls (and their relationship to perhaps children, or dogs) in reacting to this event. A machine learning system can (generally) only draw upon cases in their training set in determining a response. The effect is that a human is able to respond to events which have never previously occurred, in their or anyone’s experience; machine learning systems are, as yet, not reliable in this regard. This is utterly different to the problem faced by, say, a Go-playing ML system—the rules (the world model) of Go can be written in a single page of text. It has taken some time for ML researchers to fully appreciate the difference between these two problems.

A third problem is the power cost of computation required for ADAS. It is obviously difficult to anticipate the power cost of something that hasn’t been achieved yet, but the table below gives typical figures. For context, the 2019 Tesla full self driving (FSD) chip is reported to achieve 36 TOPS at 72 W power consumption (table 4).

These figures raise questions as to the extent to which ADAS systems will reduce the power and range available, particularly for electric vehicles. One model suggests a range reduction of the order 9%–20% for the
<figure><img src="image_34.png" /><figcaption>**Figure 31.** The SAE ADAS model. Note that levels 0–2 depend on continuous monitoring by the driver, whereas 3–5 do not. The customary vision of an autonomous car would be ADAS level 5—a car which is able to be autonomous in all environments without any human supervision or intervention.</figcaption></figure>

<table><caption>**Table 4.** Compute requirements for various ADAS levels (source: horizon robotics).</caption><thead><tr><td>ADAS capability</td><td>Compute requirements</td></tr></thead><tbody><tr><td>L2</td><td>2 TOPS</td></tr><tr><td>L3</td><td>24 TOPS</td></tr><tr><td>L4</td><td>320 TOPS</td></tr><tr><td>L5</td><td>4000 + TOPS</td></tr></tbody></table>

control systems for level 4 ADAS [446], which is not insignificant, particularly for electric vehicles where range is a critical issue.

### 23.3. Advances in science and technology to meet challenges

The perceptive reader will see that the problems facing the self-driving car industry are those which are addressed by neuromorphic engineering: modeling and reproducing human-quality perception; building cognitive models of the world; and reducing the power required for real-time perception cognition-action loop computation.

There has been some interest in using neuromorphic DVS [447] for ADAS vision, but this is hampered by the low spatial resolution of current DVS sensors (which has a complex relationship to the spatio-temporal event processing capacity of the interfaces and downstream processors used) and a perception, perhaps inaccurate, that ADAS will always require conventional (static, framed) sensors, and therefore the inclusion of DVS systems in the senior pack is a costly redundancy. This problem may be solved in due course by hybrid DVS/static imagers. In the bigger picture, the benefits offered by the high dynamic range and spatio-temporal resolution of DVS imagers has led to a general interest in event-based visual processing, which shows some potential to improve ADAS vision processing independently of the use of DVS.

Perhaps the most important feature of human vision which enables driving is the extraordinary resolution of the fovea, coupled with saccadic motion to apply this resolution to small regions of interest (such as pedestrians). There is significant (mostly unpublished) interest in using models of human salience [448] and attention to direct machine vision to areas of the visual (imager) field which require extra or high-resolution processing.
Given the real-time nature of ADAS computation, and the necessity to process correlated streams of visual and 3D point-cloud data (from lidar systems), there is some expectation that event-based neuromorphic computation may be more suitable than current GPU-type computational hardware. At least one neuromorphic event-based hardware startup is focused on real-time vision processing for this purpose [449].

In terms of building cognitive models of the world, we are reaching a point where brute-force approaches to ML are producing diminishing returns. Language models such as GPT-3 [450] can produce impressive passages of text, but it becomes clear that there is no real insight being generated; and these models are trained on orders of magnitude more text than any human could assimilate in a lifetime, suggesting that there is an unfilled deficiency in the model. Neuromorphic approaches such as Legendre memory units [451] are offering equal performance to GPT-3 architectures with 10× lower training and memory requirement, suggesting that this may help to close this gap. Similarly, the use of neuromorphic hardware such as Intel's Loihi [202] and GML's GrAIOne chips [449], which are strictly event-based and intrinsically sparse, may provide a computational platform that enables these more biologically realistic machine learning methods.

### 23.4. Concluding remarks

Achieving truly autonomous vehicles is something of a holy grail for engineering in the current era. Recently, there has been a reckoning that it is unlikely to be solved by incremental improvements based on current technologies. Neuromorphic engineering offers exactly the technological leaps that are required in perception, computation and cognition in order to achieve this goal. In particular, building machine vision systems that acknowledge and model the unique features of human vision, and building computational systems that exploit the event-based sensory flow that is common to both human and ADAS systems, seem like clear areas for fruitful research.
# 24. Olfaction and chemosensation

Thomas A Cleland

Cornell University

## 24.1. Status

Artificial olfactory systems were early adopters of biologically-inspired design principles. Persaud and Dodd constructed an electronic nose in 1982 based explicitly on the principles of the mammalian olfactory system—specifically, the deployment of a diverse set of broadly-tuned chemosensors, with odorant selectivity arising from a convergent feature detection process based on the pattern of sensor responses to each odorant [452]. Such cross-sensor patterns, inclusive of sampling error and other sources of noise, invite machine learning strategies for classification. Gardner and colleagues subsequently trained ANN to recognize odorant-associated response patterns from chemosensor arrays [453], and constructed a portable, field-deployable system for this purpose [454].

The biomimetic principle of chemical sensing by arrays of partially selective chemosensors has remained the state of the art [455, 456]. Arrays obviate the need to develop highly selective sensors for analytes of interest, as high specificity can be readily achieved by the deployment of larger numbers of partially selective sensors [457]. Moreover, such systems are responsive to a wide range of chemical diversity (odorant *quality space*; figure 32), enabling the identification of multiple chemical species and diagnostic odorant mixtures and effectively representing their similarity relationships. The intrinsic redundancy of such chemosensor arrays also renders their responses more robust to contamination or interference, provided the analysis method is able to use the redundant information effectively. In contrast, strategies for post-sampling signal processing and analysis have varied. Typically, chemosensor array responses are conditioned by electronic preprocessors and then analyzed by one of a range of methods including linear discriminant analysis, principal components analysis, similarity-based cluster analyses, and support vector machines, along with a variety of artificial neural network-based techniques [455, 456, 458, 459]. However, more directly brain-inspired techniques also have been applied to both the conditioning and analysis stages of processing. For example, the biological olfactory bulb (OB) network (figure 33) decorrelates similar inputs using contrast enhancement [460]. When applied as signal conditioning to artificial sensor data, this operation improved the performance of a naïve Bayes classifier [461]. Similarly, inhibitory circuit elements inspired by the analogous insect antennal lobe (AL) have been deployed to enhance the performance of support vector machines [459, 462]. Finally, fully neuromorphic circuits for analysis and classification have been developed that are based directly on OB/AL circuit architectures (first by [463]; reviewed in [464]; more recently [465, 466]). These approaches are discussed below.

## 24.2. Current and future challenges

In many ways, the development of functional/deployable neuromorphic systems is presently limited by algorithms. The energetic and efficiency benefits of neuromorphic engineering—arising from strictly local computation, the colocalization of memory and compute, and spike-mediated communication—are clear in principle, but these properties demand qualitatively new computational strategies to address real-world problems effectively. Developing and optimizing these algorithms for particular tasks of interest is a central challenge for contemporary neuromorphic design.

One strategy for developing effective neuromorphic algorithms is to adapt circuit motifs from the biological brain. Armed with well-developed computational circuit models of olfactory brain structures, neuromorphic olfaction has embraced this strategy, incorporating features such as layered columnar organization, recurrent lateral inhibition, temporal dynamics, spike phase coding, distinct classes of excitatory and inhibitory neurons, custom synaptic learning rules, and adaptive network expansion [460]. Notably, even within these biomimetic constraints, algorithms can differ substantially. For example, lateral inhibition is often applied to decorrelate inputs, but also has served in different implementations to implement attractor dynamics [458, 463, 466], unsupervised clustering [465], and attractor-based denoising [466] in service to different functional goals.

The present challenge is to adapt these biomimetic algorithms to practical applications. Neuromorphic technology is well suited for low-power edge devices, putting a premium on properties like rapid, online learning and adaptation to local conditions that may be partially or wholly unpredictable. Notably, both properties are weaknesses of deep networks [466], which require extensive training with examples of all planned targets including reasonable estimates of predicted future variance. Fundamentally, online learning in neuromorphic olfaction arises from *selective plasticity*, in which the network architecture directs the potentially disruptive effects of new learning to specific circuit elements, coupled with *adaptive network expansion*, which expands the physical network to encompass this new learning and render it accessible. Systems with sufficient
<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

capacity for expansion can exhibit *lifelong learning* capabilities. We have referred to this collection of properties as *learning in the wild* [460, 467], and focused on the capacity of such olfaction-inspired algorithms to learn targets from one- or few-shot learning and identify known targets amidst unpredictable interference [466], function in statistically unpredictable environments [468], and mitigate the effects of sensor drift and decay [467]. Notably, working neuromorphic olfaction algorithms have been deployed on diverse edge-compatible hardware platforms including Intel Loihi, IBM TrueNorth, fieldprogrammable gate arrays, and custom neuromorphic devices [458, 463, 466, 469, 470].

24.3. Advances in science and technology to meet challenges

The theoretical performance capacities of machine olfaction are not clearly constrained by anything short of
the fundamental signal to noise limits of their deployed environment. The basic capacity to distinguish among
similar odorants can be steadily increased by deploying arrays with larger numbers of different chemosensors,
provided those chemosensors are responsive to a range of relevant ligands and distinguish between some of
them (figure 32; [457]). The commensurate expansion in network size is tractable on neuromorphic devices
in terms of both execution time and energy expenditure (illustrated on Intel Loihi by [466]). The redundancy
derived from sensors' overlapping chemoreceptive fields also offers improved resistance to interference from
other odorant sources. The actual capacity for such signal restoration under noise depends on the development of circuits that leverage this capacity, and while early efforts are promising [466], there is substantial room for algorithm improvement, such as integrating the pattern completion and clustering capabilities of piriform cortex circuitry, developing cognitive computing methods such as hierarchical category learning to optimize speed-precision tradeoff decisions, and improving performance in the wild. Making these capacities robust requires the development of larger-scale chemosensor arrays, including compact, high-density arrays that can be deployed in the field. Different sensor technologies, optimized both for different sample phases (gas, liquid) and different chemoreceptive ranges of sample quality (e.g., food odors for quality control, toxic gases for safety), will be required. Large libraries of candidate sensors can be screened [455, 471], reducing the need for predictive models of sensors' chemoreceptive fields in this process. However, molecular imprinting technology, developed to produce highly specific chemosensors, now provides this capacity [472], and in principle could be adapted to produce broader receptive fields by imprinting analyte mixtures.

Neuromorphic circuits are not readily adaptable to arbitrary tasks; the domain-specific architectures that underlie their efficient operation also delimit the range of their applications. Olfaction-inspired networks are not limited to chemosensory applications [467], but they are not likely to be effective when tasks do not match their structural priors. However, the characterization and analysis of such fully functional neuromorphic circuits enables the identification and extraction of computational motifs, yielding toolkits that can be intelligently applied to new functional circuits. Moreover, new techniques for spike-based gradient descent learning have successfully demonstrated few-shot learning in neuromorphic circuits preconfigured for the task domain by transfer learning [317]. The design of task-specific neuromorphic circuits in the future is likely to depend on combinations of these strategies, with qualitative circuit elements drawn from theory and generalized domains established therein via emerging optimization strategies.

## 24.4. Concluding remarks

Machine olfaction has been an early and successful adopter of neuromorphic strategies for sampling and computation, in part because of the detailed elucidation of olfactory system networks by experimental and computational neuroscience. Larger-scale sensor arrays and the continued development of postsampling neuromorphic circuitry for signal conditioning and contextually-aware category learning in the wild are critical emphases for near-term progress. More broadly, the benchmarking and analysis of the various computational motifs contained in these functional biomimetic architectures may address broader theoretical and task-dependent questions such as where and when to gate plasticity in selectively plastic architectures, and the transformative capacities of local learning rules deployed within particular network contexts.

## Acknowledgements

This work was supported by NIH/NIDCD Grant R01 DC014701.
## 25. Event vision sensors

Christoph Posch

Prophesee

### 25.1. Status

Neuromorphic event-based (EB) vision sensors take inspiration from the functioning of the human retina, trying to recreate its visual information acquisition and processing operations on VLSI silicon chips. The first device of this kind out of C Mead's group at Caltech, named the 'Silicon Retina', made it on the cover of Scientific American in 1991 [473]. In contrast to early more biologically faithful models, often modelling many different cell types and signalling pathways, in turn leading to very complex designs with limited practical usability, in recent years more focus has been put on the creation of practical sensor designs, usable in real-world artificial vision applications. In [474], a comprehensive history and state-of-the-art review of neuromorphic vision sensors is presented.

Today, the majority of EB sensor devices are based on the 'temporal contrast' or 'change detection' (CD) type of operation, loosely mimicking the transient magno-cellular pathway of the human visual system (figure 34). In contrast to conventional image sensors, CD sensors do not use one common sampling rate (=frame rate) for all pixels, but each pixel defines the timing of its own sampling points in response to its visual input by reacting to changes of the amount of incident light [429, 477, 478]. Consequently, the entire sampling process is no longer governed by an artificial timing source but by the signal to be sampled itself, or more precisely by the variations over time of the signal. The output generated by such a sensor is not a sequence of images but a quasi-time-continuous stream of pixelindividual contrast events, generated and transmitted conditionally, based on the dynamics happening in the scene. Acquired information is encoded and transmitted in the form of data packets containing the originating pixel's X, Y coordinate, time stamp, and often contrast polarity. Other families of EB devices complement the pure asynchronous temporal contrast function with the additional acquisition of sustained intensity information, either pixel individually [475] or in the form of frames like in conventional image sensors [476].

Due to the high temporal precision of acquired visual dynamics, inherent data sparsity, and robust high dynamic range operation, EB sensors gain increasing prevalence as visual transducer for artificial vision systems in applications where the need for high-speed or low-latency operation, uncontrolled lighting conditions and limited resources in terms of power budget, post-processing capabilities or transmission bandwidth, coincide, e.g. in various automotive, IoT, surveillance, mobile or industrial use cases [375].

### 25.2. Current and future challenges

Despite their undeniably beneficial characteristics, EB sensors face challenges regarding practical usability and competitiveness in an industrial product. Given the non-deterministic asynchronous nature of the data generation process, leading to e.g. non-constant data rates out of the sensor, the topic of integration of an EB sensor with post-processing into a vision system raises different questions around on-chip event data preparation and interfacing.

Competing technologies for EB vision systems range from conventional computer vision to radar, lidar, ultrasound, passive infrared (PIR) and more. The challenges to make EB a successful technology are related to (1) electro-optical performance of the sensor itself, (2) system integration issues, and (3) challenges around the topic of post-processing for extracting the relevant information from the event data stream.

Pixel size is one of the most important properties for competitiveness with respect to conventional image sensor-based systems. Silicon area, optical format, compatibility with standard optics, camera module dimensions, and form factors dominate cost and applicability of the vision system. Pixel area limits the sensor resolution but also influences the dynamic range of the sensor, in particular its sensitivity at low light levels, which are important factors in key applications such as automotive, mobile or IoT. Thanks to semiconductor technology advances including 3D integration, back-side illumination and improving photodiode efficiency, continuous progress is being made on these issues. With increasing pixel array sizes, the throughput of the sensor readout system and data interface are becoming increasingly relevant to retain the pixel data temporal precision in high-speed/low-latency applications. Power consumption is a key aspect in many artificial vision applications, particularly at the edge. EB vision systems promise to deliver low-power operation thanks to the data sparsity and related efficiency in sensing and processing.

Processing of EB sensor data can be coarsely divided into (1) algorithms that prepare and optimize the raw pixel event data for more efficient transmission and processing on an (external) computation platform, and (2) algorithms that extract higher-level application-specific information out of the event data, to solve a
<figure><img src="image_37.png" /></figure>

vision task such as e.g. object detection, classification, tracking, optic flow, etc. The first group is preferably implemented close to where the raw data are generated, i.e. near-sensor or in-sensor. Typically implemented in an on-chip HW data pipeline, algorithms pre-process the raw pixel data for more efficient transmission and post-processing, also with respect to memory access and processing algorithms requirements. This data conditioning pipeline can include functions such as recoding, formatting, rearranging, compressing, thinning, filtering, binning, histogramming, framing etc. The latter group includes all application-specific vision processing using computer-vision and/or ML-based algorithms and compute models, typically running on some form of application processor. The question of the optimal compute fabric and architecture to be used with EB sensors is unresolved today, and the optimal choice application dependent. However, as discussed widely in other parts of this review, emerging non-von-Neumann architectures, in particular neuromorphic approaches such as SNN, are better suited to realize an efficient EB system than e.g. general purpose CPUs. Much progress is being made in this area, however challenges remain around the absence of well-established deep learning architectures, including training techniques, for event data, or the lack of largescale datasets.

25.3. Advances in science and technology to meet challenges

EB sensors benefit from advances in semiconductor technology. Significant progress was made since early
designs with array dimensions of about thousand pixels and pixel sizes of 1000 µm², to today, about 15 years
later, where the most advanced EB sensors have one-megapixel arrays (1000× larger) with 20 µm² pixels
(50× smaller) [478] (figure 35). In contrast to conventional image sensors, typically having one photodiode
and 4 or 5 transistors in each pixel, pixels of EB sensors are small analog computers, typically using 50+ tran-
sistors for the required signal processing and communication functions. Early EB sensor designs employed
standard CMOS processes and later front-side illuminated (FSI) CIS processes. All transistors and other devices
needed to be placed next to the photodiode, leading to large pixel sizes and low fill factors. The introduction of
back-side illuminated CIS processes relaxed the situation and today, 3D wafer stacking combined with small
pitch metal bonding allows to place most of the pixel circuitry underneath the photodiode. Latest generation
EB sensors today reach pixel pitches below 5 µm and fill factors above 75% [478].
<figure><img src="image_38.png" /><figcaption>Figure 35. Evolution over time of pixel pitch and array size of CD-based EB sensors.</figcaption></figure>

Following the CMOS technology and integration roadmaps will yield EB devices with increasing industrial applicability. Further advances in production and packaging technologies like triple wafer stacking, die-stacking system-in-package and wafer-level optics will support the trend to autonomous ultra-low power/small form-factor edge perception devices and AI systems where the sensor is highly integrated and tightly packaged with pre-processing and application processing, thereby significantly reducing power consumption and transmission bandwidth requirements of an artificial vision system, e.g. in IoT, mobile or perception networks applications.

A big impact on the usability and competitiveness of EB systems is expected to come from future advances in neuromorphic computing and event-based processing techniques. SNN are a natural fit for post-processing to the data generated by EB sensors [202, 479]. But the sparse data output of EB sensors is also a good match to future hardware accelerators for conventional DNN that exploit activation and network sparsity [480].

Recently, new kinds of neuromorphic vision devices beyond CMOS have been demonstrated, exploiting different electro-optical material properties and fabrication techniques to further advance the tight integration of sensing and processing, often combining photon transduction and analog neural network (ANN) functions into a single fabric [129, 130, 481, 482]. Even though these devices are in their early proof-of-concept phase, interesting and promising results have already been demonstrated.

### 25.4. Concluding remarks

Integrating event-based vision sensing and processing with neuromorphic computation techniques is expected to yield solutions that will be able to penetrate the artificial vision market and gain considerable market share in the coming years [483]. This new class of extremely low-power and lowlatency AI systems could, in a world where power-hungry deep learning techniques are becoming a commodity, and at the same time, environmental concerns are increasingly pressuring our way of life, become an essential component of a sustainable society.
# 26. Neuromorphic audition

ShihChii Liu

University of Zurich and ETH Zurich

## 26.1. Status

Neuromorphic audition technology is inspired by the amazing capability of human hearing. Humans understand speech even in difficult auditory scenarios and using a tiny fraction of the brain's entire 10 W. Matching the capability of human hearing is an important goal of the development of algorithms, hardware technology and applications for artificial hearing devices.

**Brief history:** human hearing starts with the biological cochlea which uses a space-to-rate encoding. The incoming sound is encoded as asynchronous output pulses generated by a set of broadly frequency-selective channels [484]. For frequencies below 3 kHz, these pulses are phase locked to the frequency [485]. This encoding scheme leads to sparser sampling of frequency information from active frequency channels instead of the maximal sampling rate used on a single audio input. The first silicon cochlea designs starting with the work of Lyon and Mead (electronic cochlea) [486], model the basilar membrane (BM) of the cochlea by a set of coupled filter stages. Subsequent designs include those with better matching properties for the filter stages and using coupled filter architectures ranging from the originally proposed cascaded type modeling the phenomenological output of the cochlea [486], to a resistively-coupled bank of bandpass filters that models the role of the BM and the cochlear fluid more explicitly [487, 488].

Later designs include models of the inner-hair cells on the BM, that transduce the BM and fluid vibrations into an electrical signal. They are frequently modelled as half-wave rectifiers in silicon designs. Some designs include the automatic gain control mechanism of outer hair cells that are useful for dealing with larger sound volume ranges from 60–120 dB. Cochlea designs starting from the early 2000s include circuits that generate asynchronous binary outputs (or spikes) encoded using the address-event representation. Details and historical evolution of these VLSI designs are described in [487, 488]. Recent spiking cochlea designs in more advanced technologies such as 65 nm and 180 nm CMOS demonstrate better power-efficiency (e.g., <1 uW/channel in [489]). These new designs show competitive power efficiency compared to other audio front end designs that compute spectrogram features from regular samples of a single audio source.

**Importance of field:** in the early 2000s, cochlea circuits were developed for audio bionic applications [490] and models of biological auditory localization circuits [491]. With increasing prevalence of voicecontrolled devices in everyday life, neuromorphic and bio-inspired solutions can potentially be interesting because of the need for low-latency and energy-efficient design solutions in audio edge application domains.

## 26.2. Current and future challenges

**Big research issues:** neuromorphic bio-inspired features such as the sparse sampling (e.g., the nonsampling during silent pauses), the event-driven form of brain computing, the natural temporal encoding carried by the asynchronous events (pulses, or sets of pulses, within a specified time window), can enable more energy-efficient solutions for hardware-friendly models that solve an auditory task. The timing information from the asynchronous events of the spiking silicon cochleas is ideal for extracting interaural time differences, which is useful for spatial audition at lower latencies [492, 493]. Speech recognition examples that use spiking cochlea inputs include the reservoir networks of spiking neurons applied to digit recognition [494] (figure 36).

With the advent of deep learning, DNNs have been used successfully for many audio benchmarks. Even though training of these networks requires global supervised methods and large datasets, they can be applied on features from spiking cochlea events for always-on low-level audio tasks such as voice activity detection (VAD) and key word spotting (KWS). These modules can then activate the more energy-expensive audio tasks such as speech recognition which require running larger networks on the cloud. The energy savings of these neuromorphic solutions come from the event-driven way of processing, i.e., the processing is triggered by asynchronous information carried by the frequency-selective events rather than on regular time-stepped spectrogram frames; compute savings come naturally with silent pauses and non-changing inputs. Hardware versions of this combination include a complete system using an FPGA recurrent network processing cochlea spikes for a continuous digit recognition task [495] and TrueNorth spiking network platform processing audio features [496]. ASIC implementations that include both sensor and network show extremely lowpower consumption, e.g., the <1 uW ASIC VAD chip [497].

**Mapping** spiking neural network (SNN) algorithms trained for a task to SNN hardware platforms require additional considerations such as the variability of the network parameters from transistor mismatch and the timing jitter noise of the transmitted spikes from the transmission of asynchronous spikes in real-time on a specified SNN hardware platform. While more work is needed on local spikebased learning rules [498]
<figure><img src="image_39.png" /><figcaption>**Figure 36.** (A) Top subfigure shows the 64-channel cochlea spike rasters corresponding to speech sample waveform of spoken digits, '3-5' in bottom subfigure. (B) Architecture for an example audio keyword spotting task. Figure shows an ASIC block that combines the dynamic audio sensor (DAS) front-end with a continuous-valued DNN [495, 497] for an example 'wake-up' keyword spotting task. Spike outputs of the local filter channels are generated using asynchronous delta modulation [489]. The spike events can be used to drive an SNN directly [496].</figcaption></figure>

to configure SNNs to reach similar accuracy compared to continuous-valued artificial neural network (ANN) solutions on a specific task, conversion techniques that map trained ANNs to SNNs [499] and global supervised training methods for SNNs have been effective for this goal [500].

## 26.3. Advances in science and technology to meet challenges

For edge applications, besides the high energy-efficiency needed of the hardware platform, maintaining high audio task accuracy is also important. More work is needed to show that neuromorphic audition systems can be competitive for these domains, e.g., through benefits of spike events for efficient models or local learning.

Audio spectrogram features are dominant features used in conventional audio applications. Studies such as [494, 501] show that the accuracy on a simple speech recognition task using spiking cochlea features while showing lower numbers than spectrogram features for clean conditions, can maintain lower accuracy loss over decreasing signal-to-noise conditions. These ideas need further investigation for larger datasets and tasks. Features extracted from the spikes, e.g., exponential features [502], could help to determine the utility of dynamic information. The real-time extracted azimuth information allows source separation from a mixture of two speakers, e.g., by using the streamed source spikes for speaker identification and keyword spotting using deep network algorithms [502]. Future research is needed for models that operate robustly in wide dynamic range (>60 dB) auditory scenes.

More extensive work is needed to develop training or spike-based learning methods to configure for high-accuracy networks on specified audio tasks. Training directly on spiking networks instead of using conversion ANN to SNN methods would capitalize better on implicit SNN encoding capabilities. The approximation of rate codes to analog activations of ANNs is not ideal. Temporal coding methods would be more energy efficient but need extension to more complex datasets. Combination of the auditory systems together with other modalities for a robotic platform would also be interesting for future developments.

**Technology:** technology advances include the continual support of analog integrated circuit designs as technology nodes scale down for digital transistors. Of importance for low-power devices are transistors with low leakage current (for low standby power) and fabrication processes that still allow low transistor mismatch without requiring large transistor dimensions. The standby power consumption of the ASIC is an important
metric for always-on audio devices. Other challenges include algorithms that are hardware aware, e.g., to the variability of the network parameters after the ASIC fabrication, and approaches to reduce memory access or to create predictable memory access patterns to reduce energy loss from the unpredictable memory accesses of SNNs. The emerging largescale availability of high-density local memory is also an interesting component of future research for the ASIC development.

## 26.4. Concluding remarks

Progress in neuromorphic audition critically depends on advances in both silicon technology and algorithmic development. Recent ASIC prototypes that combine binary networks and spectral features report competitive low-power numbers (<1 µW) for KWS [503]. Evaluation of these solutions with ASICs that use a spiking cochlea front-end will help determine the latency-accuracy-power tradeoff between these approaches for audio edge applications. Other considerations are the system-level power of a complete audio system including microphones (current commercial audio assistants are ~1 W), that continue to benefit from the ultra-low power numbers of the audio ASICs. Also needed are event-driven algorithms to address more complex tasks beyond KWS and VAD while maintaining the energy efficiency and low latency benefits, and how we can capitalize on the new emerging highdensity memory technologies to reduce the power dissipation from unpredictable memory accesses of events/spikes. The combination of the DAS with other spiking sensors, e.g., the dynamic vision sensor, will be interesting for robotic platforms. Hybrid solutions that bring knowledge from auditory science, signal processing, and machine learning will enrich future artificial neuromorphic technology solutions for audio application domains.

## Acknowledgements

We acknowledge the Sensors Group members and colleagues who have worked on the Dynamic Audio Sensor design and audio systems. Partial funding provided by the Swiss National Science Foundation, HEAR-EAR, 200021172553.
## 27. Biohybrid systems for brain repair

Gabriella Panuccio and Mufti Mahmud

Istituto Italiano di Tecnologia, Italy

Nottingham Trent University, United Kingdom

### 27.1. Status

Biohybrid systems are established by biological and artificial components interacting in a unidirectional or bidirectional fashion. In this section, we specifically refer to neurons or brain tissue as the biological component of biohybrid systems for brain repair.

The first demonstration of a biohybrid dialogue was achieved *in vitro* at the beginning of the 1990s by Renaud-LeMasson and colleagues, who established a communication between a biological neuronal network and a computational model neuron [504]. Soon after, Chapin and colleagues brought the biohybrid paradigm to the *in vivo* setting by providing the first proof of concept of interfacing the brain with a robotic end-effector [505], a paradigm that has recently become a reality in the clinical research [506].

Biohybrid systems are now a widespread approach to address brain dysfunction and devise novel treatments for it [507]. Representative examples are electronic devices coupled to biological neurons *in vitro* [219] or to the brain *in vivo* [508] and establishing a bidirectional communication through a closedloop architecture. A key feature of such systems is the real-time processing and decoding of neural signals to drive an actuator for brain function modulation or replacement. To this end, enhancement of biohybrid systems with AI is the emerging strategy to achieve an adaptive interaction between the biological and artificial counterparts. Neuromorphic engineering represents the latest frontier for enhancing biohybrid systems with hardware intelligence [509] and distributed computing [510], offering unprecedented brain-inspired computational capability, dynamic learning of and adaptation to ongoing brain activity, power-efficiency, and miniaturization to the micro-scale. In particular, the intrinsic learning and adaptive properties of neuromorphic devices present the key to bypass the typical trial-and-error programming along with the stiff pre-programmed behaviour of current brain implantable devices, such as those used for deep-brain stimulation. In turn, such a unique potential enables surpassing the drawbacks of current mechanistic approaches with a phenomenological (evidence-based) operating mode. Overall, these features serve as an asset to attain a physiologically-plausible interaction between the biological and artificial counterparts.

The latest avenue for biomedical applications is neuromorphic-based functional biohybrids for brain regeneration. These are hybridized brain tissue grafts (figure 37), wherein the neuromorphic counterpart(s) emulate and integrate brain function, aiming at guiding the integration of the biological graft into the host brain. This crucial aspect cannot be attained by a purely biological regenerative approach. Further advances in neuromorphic biohybrids are thus expected to bring unparalleled strategies in regenerative medicine for the brain: by providing symbiotic artificial counterparts capable of autonomous and safe operation for controlled brain regeneration, they herald a paradigm shift in biomedical interventions for brain repair, from interaction to integration.

### 27.2. Current and future challenges

As biohybrid grafts for brain repair are fully integrated autonomous constructs, their achievement entails the full physical integration of the neuromorphic counterpart(s) within the bioengineered brain tissue to be grafted. However, while neuromorphic neuroprostheses coupled to bioengineered brain tissue may constitute in themselves a biohybrid system, they do not make up a biohybrid graft in the strict sense, being neuroprostheses conceived as an exogenous body (figure 38).

In functional biohybrids, the integrated neuromorphic counterpart(s) must not perturb the mechanical equilibrium of the biological neurons; hence, depending on the intended application (i.e., extracellular or intracellular biosensing from neurons and their networks), these must be scaled down to meet the size of a small neuronal ensemble ($<$150 µm [511]) or even to achieve intracellular residency ($<$3 µm [512, 513]). In both cases, the primary challenge, and the pre-requisite, is the aggressive miniaturization of the integrated neuromorphic devices(s) without compromising their computational capability. Thus, functional biohybrids entail the realization of neuromorphic dust. Further challenges arise due to the physical confinement of the neuromorphic counterparts within the biohybrid graft, stemming in the requirement of:

(a) Power-autonomy: device powering cannot rely on a wired power supply unit, such as commonly used subcutaneous batteries: while continuous device operation without the need of battery replacement is of utmost importance in brain regeneration, wiring neural dust is unfeasible (the devices are ultrasmall
<figure><img src="image_40.png" /><figcaption>**Figure 37.** Concept of functional biohybrids for brain regeneration. Functional biohybrids merge concepts from regenerative medicine (rebuild of brain matter) and neuromorphic neuroprosthetics (adaptive control of brain function). The symbiotic interaction between the biological and artificial counterparts in the biohybrid graft is expected to achieve a controlled brain regeneration process.</figcaption></figure>

and physically inaccessible). Further, the operation of an autonomous system, by definition, should not depend on external components.

(b) Wireless operation: this is required to follow the graft's evolving function during the regeneration process, to enable wireless device re-programming and hardware failure monitoring.

(c) On-chip learning, supported by application-specific integrated circuits for advanced signal processing, to follow the evolving temporal dynamics of the graft during its integration within the host brain, without the aid of an external controller.

(d) Bioresorbable property: in aiming at healing brain damage, the neuromorphic counterparts should be regarded as a temporary aid in the process. Thus, they should be removable upon completion of brain repair. While non-invasive micro-surgery techniques, such as high-intensity focused ultrasound, may permit removal of mm-sized devices, this is not technically feasible in the case of ultrasmall (and, even more so, intracellular) devices. Thus, particularly relevant to functional biohybrids is that the neuromorphic counterparts should be bioresorbable.

### 27.3. Advances in science and technology to meet challenges

**Miniaturization.** The device size will reflect the complexity of the required architecture. Such a complex system requires integrated interfacing circuits, for which the roadmap toward 1 nm-gate length of CMOS technology [514], along with avant-garde fabrication and integration technologies hold promise to meet this outstanding challenge. Further, novel materials for improved performance are expected to emerge in the near future to enable higher computational capability within smaller sizes. Organic materials hold great promise to merge advanced computation with extreme miniaturization [515].

**Power autonomy.** While radiofrequency [516] and ultrasonic waves [517] are successfully deployed for powering deep biomedical implants, they require an external transmitter. Energy harvesting from the human body may represent the strategy of the future [518], wherein ATP seems to be the most promising solution to a long-standing issue [519]. However, caution must be made to avoid negative physical effects on the patient, as the amount of harvested energy must be much less than what available. Thus, the device components must be ultra-low-power and energy efficient.
<figure><img src="image_41.png" /><figcaption>**Figure 38.** Challenges to achieve functional biohybrids for brain regeneration.</figcaption></figure>

**Wireless operation.** While autonomous operation is a key feature of the neuromorphic dust, the need of patient monitoring, device fine-tuning, and hardware failure checks should not be underestimated for guaranteeing the patient's safety. To this end, wireless access to the device is fundamental. Thus, dedicated integrated circuits are required, which must be ultrasmall so not to introduce bottlenecks in device miniaturization. As stated above, advanced CMOS technology holds promise to enable these wireless features in neuromorphic dust. Further, protocols tailored to energy efficient wireless communication are needed.

**On-chip learning.** Understanding spatiotemporal patterns is a key feature to address the evolving dynamics of neuronal networks and reverse-engineer brain dysfunction. So far, these features have been achieved by pre-programming and the use of a microcontroller [520]. Further advances must be made in order to achieve the same level of performance through on-chip learning. This would enable to address the inter-individual variability of the human brain while overcoming the drawbacks of trial-and-error (re)programming and of the need of a wired controller.

**Bioresorbable materials.** The device materials must be fully biocompatible so not to release cytotoxic compounds in the patient's brain. While outstanding advances have been made in the biosensors field [521], a major efforts must be put in the field of neuromorphic engineering, where the performance of the device strongly depends on materials. In this regard, organic materials may present the key to beat this challenge.

## 27.4. Concluding remarks

Much progress has been made to incorporate artificial devices within neural spheroids [522], or inside single living cells [512, 513]. However, these are biosensors of simple architecture, far from the complexity of a neuromorphic system. The achievement of functional biohybrids thus requires the parallel effort from diverse disciplines to address the numerous challenges linked to them. The exponential progress in fabrication and miniaturization strategies, energy harvesting, learning algorithms, wireless technology, and bioresorbable bioelectronics heralds the feasibility of cutting-edge biohybrid neurotechnology for controlled and safe brain regeneration.

## Acknowledgements

This work was funded by the European Union under the Horizon 2020 framework programme through the FET-PROACTIVE project HERMES - Hybrid Enhanced Regenerative Medicine Systems, Grant Agreement No. 824164.
# 28. Embedded devices for neuromorphic time-series assessment

Arnab Neelim Mazumder, Morteza Hosseini and Tinoosh Mohseni

University of Maryland, Baltimore County

## 28.1. Status

Neuromorphic computing aims to mimic the brain to create energy-efficient devices capable of handling complicated tasks. In this regard, analysis of multivariate time-series signals has led to advancements in different application areas ranging from speech recognition and human activity classification to electronic health evaluation. Exploration of this domain has led to unique bio-inspired commercial off-the-shelf device implementations in the form of fitness monitoring devices, sleep tracking gadgets, and EEG-based brain trauma marker identifying devices. Even with this deluge of work over the years, the necessity of evolving the research direction with day-to-day needs relating to this sphere is still pivotal. The key idea behind the wealth of research in these domains comes from the fact that it is very difficult to generalize human abilities and activities, and it is even more difficult to create devices that can operate at a level as accurate as human-level perception. This is where contemporary machine learning and the more modern deep learning frameworks shine. The current scenario of using automated devices for a variety of health-related applications requires that these devices become more sensitive, specific, user-friendly, and lastly accurate for their intended tasks. This relates to further advancements in the region of algorithm construction and constraint-based design of implementable hardware architectures. The current crop of research in this area investigates DNNs architectures for the purpose of feature extraction, object detection, classification, etc DNN models utilize the capacity of CNNs, recurrent neural networks (RNNs), and even to some extent fully connected layers to extract spatial features for time-series assessment which was previously exhaustively calculated via different hand-engineered feature extraction techniques coupled with simple classification algorithms. Along with this, RNNs and their advanced equivalents in the form of long short term memory networks (LSTMs) and gated recurrent unit has also been integrated into the deep learning architectures to handle timeseries signals. The idea behind this integration stems from the fact that RNNs and LSTMs are modeled in such a way that they can keep track of previous instances of the input data in order to make a prediction, which makes these architectures very effective for pattern and dependency detection within the time-series data. The other aspect of developing these diverse DNN models is to make them readily implementable in terms of hardware accelerators and therein lies the issue of hardware constrained efficient designs. As a consequence, the computation and model size specifications of different hardware-oriented approaches will result in the advancement of application-oriented software designs which will, in turn, increase the reliability and efficiency of these embedded devices.

## 28.2. Current and future challenges

There are several challenges associated with managing time-series signals for classification or recognition tasks. One of the foremost issues of time-series classification is to make these signals interpretable by the DNNs as these signals contain multiple variables relaying information about concurrent actions and it is difficult to process these signals in their raw form. Authors in [523] proposed a solution to this problem by transforming these multimodal signals into windowed images based on their sampling frequencies. Another obstacle that is related to time-series analysis pertains to skewed or imbalanced information belonging to multimodal variables as the data collection procedure with different sensors might not always be the same. As a way around, a common practice is to use weighted sampling of the input features during the training of the DNN models so as to balance the impact of all features. Pruning outliers in the dataset by eliminating unnecessary sensor data can alleviate this problem as demonstrated by the authors in [524], however, it is not always feasible to delete multimodal information as the sensor data for multiple variables might be correlated. In addition to this, many of the software frameworks dedicated to time-series classification do not consider the large computation overhead of the DNNs. This has a significant impact when these frameworks are replicated on to resource-limited and low-power embedded platforms where the use of off-the-chip-memories becomes essential. As a result, the performance remains limited by the memory bandwidth while the power consumption stays high due to the rapid accessing of off-the chip memories. The extent of these complications has introduced shallow networks [525], approaches to quantizing model parameters [526] along with ternary [527] and binary [528] models that focus on reducing the memory overhead for efficient resource-constrained hardware accelerator implementation. Authors in [529] provide an example of a fixed-point CNN classifier involving 4 bit fixed point arithmetic that suggests negligible accuracy degradation and authors in [530] present fast BNN inference accelerators to meet the FPGA on-chip memory requirements. Reducing memory footprints in hardware accelerators is also tied up to the cost-effective designing of memory units. On top of this, managing and limiting frequent accesses of these memory units also contribute to latency, power, and energy efficiency as a whole.
<figure><img src="image_42.png" /><figcaption>**Figure 39.** The deep learning framework takes in windowed images of the raw multimodal time-series signals as input to the convolutional layers. Correspondingly, feature extraction is achieved in convolutional layers which results in a two-dimensional feature map. The pooling layers contribute to reducing the feature map size while keeping the spatial features intact. This two-dimensional pooled feature map is reshaped to have one-dimensional form so that it can be forwarded to the next fully connected layers. Finally, the last fully connected layer will have neurons equal to the number of outputs as desired by the application. Furthermore, with regards to multi-input model, supplementary information coming from a separate model can be concatenated with the one-dimensional feature map to bolster the inference accuracy.</figcaption></figure>

Thus, a critical challenge in terms of hardware design is to maintain high frequency and energy efficiency with low energy consumption.

## 28.3. Advances in science and technology to meet challenges

Deep learning frameworks have been widely successful for classifying time-series signals. However, the challenges mentioned in the previous section make this task ever more difficult. To further boost the performance of deep learning methods for time-series data, some form of digital signal processing is commonly required. To this extent, a common practice is to convert these raw waveforms into windowed overlapping time-series frames. A sliding window of some specific size along with a stepping size is passed through all variables, creating a set of images of shape as desired by the user. Since most time-series signals contain label information at precise time intervals, it is fairly easy to determine the label of the images. Another facet of dealing with time-series signals requires feature extraction relevant to the application that is being targeted. With classical machine learning algorithms, this was achieved using several mathematical and analytical processes to determine the correlation between variables. In contrast, one of the strengths of using CNNs or RNNs in deep learning ensures that the relevant features are being extracted in image or time-space. A general practice in time-series classification is to deploy CNN or RNN layers in conjunction with pooling layers as illustrated in figure 39. The pooling layers reduce the feature map size so that the cost of computation for the following fully connected layers is minimized. Additionally, the feasibility of hardware deployment of these deep learning algorithms depends on the computational complexity and size of these architectures. It is imperative that these frameworks are reduced in size via quantization, pruning, or by making the networks shallow in the first place so that they fit on embedded devices with small memories. Hence, there comes a point where the
<figure><img src="image_43.png" /><figcaption>**Figure 40.** This figure illustrates the trend of energy efficiency against model size of different deep learning architectures deployed on the low power Artix-7 100t FPGA platform which has a memory of 1.65 Mb. The applications focused here are EEG detection [532], human activity recognition [523], stress detection [523], tongue drive systems [523] along with cough and dyspnea detection as part of respiratory symptoms recognition [531]. Depending on the model size, the frameworks can be tiny or large whereas the energy efficiency is dictated by the performance of the design. In the same vein, the plot also shows the device inference accuracy for the different models ranging from 86% up to 98% which further justifies that these architectures are specific enough for low power embedded deployment.</figcaption></figure>

designer has to find the sweet spot between the accuracy of the model and the practicality of its size being suit-
able for low power embedded platforms while also ensuring that the energy efficiency of the target device is
also satisfactory. Figure 40 shows a comparison among different models with a variety of applications for their
model size, classification/detection accuracy, and energy efficiency which establishes that depending on the
application, deep learning models can fit on low-power embedded devices with standard performance. Also,
a modification to these frameworks can take in additional information in the form of vectors from a separate
model to enhance the overall accuracy of the model as demonstrated in figure 39.

**28.4. Concluding remarks**

Human-related time-series data analysis encompasses a wide range of tasks including speech recognition, keyword spotting, health monitoring, and human activity recognition to name a few. This also allows the dedicated development of embedded devices suited for accelerating such tasks. Challenges in processing such time-variant data for device implementation range from pre-processing the raw signals and removing noise and outliers to interpreting long and short dependencies that exist within the nature of the data. Windowing the continuous stream of data into overlapping frames to be processed using a simple DNN or CNN is a common practice for real-world applications in which the long dependencies in data are negligible. On the other hand, novel approaches such as RNNs and LSTMs can improve the overall confidence of analysis for time-series data with long dependencies. When implementing all these methods on resource-bound hardware in which power, energy, memory footprint, and application latency are all limited, it is of utmost importance to design deep learning algorithms with small model sizes and low computation that meet all the application requirements and hardware limitations. In conclusion, there must be a trade-off between performance and implementation feasibility to justify the use of low-power embedded devices to replicate deep learning applications of time-series assessment.
# 29. Electromyography processing using wearable neuromorphic technologies

Elisa Donati

University of Zurich and ETH Zurich, Switzerland

## 29.1. Status

Electromyography (EMG) is a neurophysiological technique for recording muscle movements. It is based on the principle that whenever a muscle contracts, a burst of electric activity is propagated through the close tissue. The source of the electrical signal in EMG is the summation of action potentials of motor units (MUs) [533]. An MU is composed of muscle fibers innervated by axonal branches of a motor neuron, that is intermingled with fibers of other MUs. The recorded electric activity is linearly correlated to the strength of the contraction and the number of recruited MUs. EMG signals can be acquired both invasively, using needle electrodes, and superficially, by placing electrodes on the skin—called surface EMG (sEMG).

EMG signals have been and are relevant in several clinical and biomedical applications. In particular, they are extensively employed in myoelectric prosthetics control for classifying muscle movements. Wearable solutions for this application already exist, but they have a large margin for improvement, from increasing the granularity of movement classification to reducing computational resources needed and consequently power consumption.

Like any other signal, EMG is susceptible to various types of noises and interferences, such as signal acquisition noise, and electrode displacement. Hence, a pre-processing phase is the first step to perform proper signal analysis, which involves filtering, amplification, compression, and feature extraction both in time and frequency domains [534]. The mainstream approach for movement classification is machine learning (ML), which delivers algorithms with very high accuracy [535], although the high variability in test conditions and their high computational load limit their deployment to controlled environments. These drawbacks can be partially solved by using deep learning techniques that allow for better generalization to unseen conditions but remain computationally expensive, requiring bulky power-hungry hardware, that hinder wearable solutions [536].

Neuromorphic technologies offer a solution to this problem by processing data with low latency and low-power consumption mimicking the key computational principles of the brain [3]. Compared to state-of-the-art ML approaches, neuromorphic EMG processing shows a reduction of up to three orders of magnitude in terms of power consumption and latency [537–539], with limited loss in accuracy (5%–7%) [540, 541].

New approaches have been proposed that directly extract the motor neurons activity from EMG signals as spike trains [543]. They represent a more natural and intuitive interface with muscles but currently limit themselves by processing spikes with traditional ML techniques and do not consider the possibility of using more appropriate frameworks such as SNNs.

## 29.2. Current and future challenges

Although the performance of myoelectric prosthetics increased conspicuously in the last decade [544], they still cannot be used in daily life. The fine-grained control is in fact limited by the number of electrodes. This issue can be overcome by using high-density EMG (HD-EMG), which typically uses hundreds of electrodes, allowing to monitor larger areas and effectively increasing the precision of the measurements [545]. However, HD-EMG uses more computational resources, in terms of power and time required to classify movements and to generate motor commands. Current technologies are not able to process such an amount of data *in situ* and with low latency simultaneously. For this reason, the EMG signals are transmitted, for example via bluetooth, to a remote system that is quite bulky and heavy, making a wearable solution impractical.

Neuromorphic technologies represent a solution to all the described limitations by processing data in parallel, with low latency, and taking advantage of the low-power nature of analog computing and spiking communication, as the biological system they are inspired from. Although recent results show promising advances, the current challenge of neuromorphic technology is to fill the gap with state-of-the-art ML approaches, in terms of accuracy. One of the main reasons behind this gap is the different amount of resources invested in the respective research fields. In addition, current research that focuses on adopting ML methods and implementing them in neuromorphic hardware faces challenges governed by the unsuitability of such substrates which are primarily targeted for SNNs [542].

To get the most from neuromorphic computing we need a change of paradigm, where the neuromorphic technology can directly interface with motor neurons' spiking activity, instead of continuous sEMG signals. This represents a matching condition between inputs and outputs that optimize the information transfer between the muscle activity and the processing and control unit. The spike trains of motor neurons can be extracted from sEMG signals by means of decomposition algorithms. In particular, the spatial distribution
<figure><img src="image_44.png" /><figcaption>**Figure 41.** A concrete roadmap towards neuromorphic wearable devices.</figcaption></figure>

of MUs action potentials can be assessed with activation maps obtained from HD-EMG signals [543]. Nevertheless, current implementations are still computationally expensive, and only recently it was possible for their deployment in real-time. After the decomposition, the spike trains are translated and processed using ML methods instead of better-suited SNNs [546].

Designing neuromorphic systems able to extract and process motorneurons activity from EMG signals will pave the way to a new class of wearable devices that can be miniaturized and directly interface with the electrodes.

## 29.3. Advances in science and technology to meet challenges

A concrete roadmap towards wearable neuromorphic EMG processing, see figure 41, could be constructed with short and long-term objectives. In the short term, we should advance neuromorphic computation to bridge the gap with ML methods for EMG classification, and optimize decomposition algorithms to make them run real-time on embedded systems. In the long-term, the decomposition algorithm should be ported into a neuromorphic chip to implement a fully spiking pipeline while the technological breakthroughs in surface smart electrodes could potentially be able to record directly motorneurons' spike trains.

**Bridge-the-gap.** The first step is to understand the requirements to improve the accuracy of EMG movements classification. The front-end, which includes pre-processing and spike conversion, has the largest margin for improvement. Signal-to-spike conversion produces spike trains required by neuromorphic devices. The most common signal-to-spike converter is the delta-sigma [538] which is widely applied in biomedical applications, thanks to its lower circuit complexity compared to multi-bit ADCs. However, the delta-modulator generates a high sampling rate and larger data size that can easily push the neurons' firing rate into saturation, making them insensitive to further input variations. Furthermore, SNNs for EMG classification should be optimized and learning algorithms could make them adaptable to different patients. Embedded decomposition sEMG decomposition into spike trains is generally based on shape-based algorithms, also called template matching [547] or blind source separation algorithms [548]. The decomposition of the complex sEMG is a computationally expensive procedure in a multidimensional constraint space. To run these algorithms on embedded platforms and in real-time it is imperative to (i) reduce the complexity and (ii) optimize it for the selected digital embedded architecture (e.g. PULP platform [549]) and exploit its hardware capabilities. The extracted spike trains are then sent to a neuromorphic chip, creating a hybrid digital-analog framework for spike encoding low-power computation.

Spike-based EMG decomposition to build a fully spiking pipeline that can be integrated into a single neuromorphic chip, the MUs identification algorithm needs to be translated into a spiking version. Embedding the entire process into a single chip that can be miniaturized and connected directly to the electrodes will allow
online processing, which is optimal for real-time closed-loop applications and less vulnerable to interferences either caused by humans or the environment.

Smart electrodes another long-term game-changer would be the technological breakthroughs that will allow the single electrode to be able to record directly the activity of a single MU, removing the need for decomposition algorithms.

## 29.4. Concluding remarks

The need of improving myoelectric prosthetic control to increase the life quality of the patient poses new challenges for implementing real-time, compact, and low-power EMG processing systems. A wearable device based on neuromorphic technology can enable *in situ* EMG signal processing and decomposition, without information transfer and external computation. In particular, mixed-signal SNNs implemented on neuro-morphic processors can be integrated directly with the sensors to extract temporal data streams in real-time with low-power consumption.

This roadmap presents the specific case of prosthetic control, nevertheless, the development of this technology could reveal useful to more applications where continuous monitoring is required. In clinical settings, continuous monitoring of EMG signals can be utilized to detect degenerative diseases of motorneurons [550] even for very large time spans such as weeks or months. In rehabilitation, EMG can be used as feedback to adapt the patient training accordingly to its muscular status, after a stroke or neurological impairments [551].

With the current rate of technological and computational improvements the proposed objectives could be realistically achieved within a decade. If successfully executed, this roadmap will bring technology that will improve the quality of life for amputees and patients with motorneuron diseases.
# 30. Collaborative autonomous systems

Silvia Tolu and Roberto Galeazzi

Technical University of Denmark (DTU)

## 30.1. Status

Collaborative autonomous systems (CAS) (see figure 42) are entities that can cooperate among themselves and with humans, with variable levels of human intervention (depending on the level of autonomy) in performing complex tasks in unknown environments. Their behaviour is driven by the availability of perception, communication, cognitive and motor skills and improved computational capabilities (on/off-board systems). The high level of autonomy enables the execution of dependable actions under changing internal or external conditions. Therefore, CAS are expected to be able to: (1) perceive and understand their own condition and the environment they operate in; (2) dependably interact with the physical world despite of sudden changes; (3) intelligently evolve through learning and adaptation to unforeseen operational conditions; (4) self-decide their actions based on their understanding of the environment.

Currently, CAS (e.g., collaborative robots-cobots) show limited performances when accomplishing physical interaction tasks in complex scenarios [552]. Recent studies have demonstrated that autonomous robots can outperform the task they are programmed for, but they are limited in the ability to adapt to unexpected situations [553] and to different levels of human-robot cooperation [552]. These limitations are mainly due to the lack of generalization capabilities, i.e., cobots cannot transfer knowledge across multiple situations (environments, tasks, and interactions). One of the most viable pathways to solve this issue is to build intelligent autonomous cobots by incorporating AI-based methods into the control systems [554]. These bio-inspired controllers [555] allow taking a different perspective from the classical control approaches, which require a deeper understanding of the mechanics of the interactions and of the intrinsic limitations of the systems beforehand. Main current research directions [556] are focused on the understanding of the biological working principles of the central nervous system in order to build innovative neuromorphic computing algorithms and hardware that will bring significant advances in this field; in particular, they will provide computational efficiency and powerful control strategies for robust and adaptive behaviours.

In the next decades, there will be significant developments in CAS related to self-capabilities such as self-inspection, -configuration, -adaptation, -healing, -optimization, -protection, and -assembly. This will be a great enabler of systems acting in real-world unstructured scenarios, such as in remote applications (deep sea or space), in hazard situations (disasters), in healthcare interventions (assistive, rehabilitation, or diagnosis), and in proximity to people.

## 30.2. Current and future challenges

Several fundamental challenges demand to be addressed to enable the deployment of heterogeneous autonomous systems able to collaborate towards the achievement of common mission objectives. These challenges span across different research topics including **online mission planning** and execution for multi-agent systems under uncertainty. Future mission planners [557] should integrate several factors to determine the optimal allocation of agents to the fulfilment of the mission tasks. These factors, among many, include energy availability and depletion rates, physical capabilities of the agents, probability of failures, and amount of collaboration needed. The mission execution demands the development of a revolutionary control paradigm that enables true collaboration among CAS with different functionalities. **Cooperative control** [558] has been so far limited to consensus and synchronization to enable the coordinated dynamic evolution of mostly homogeneous multi-agent systems to perform the same type of actions. The execution of tasks in uncertain environments calls for robust learning/adaptation methods to enable baseline control systems to ensure robust cooperation and coordination of heterogeneous multi-agent robots in various real applications [558]. Another big question is how to endow CAS with a high-level of **fault tolerance** capabilities in order to ensure dependability under a wide variety of operational conditions [559]. Despite the large research effort pursued by the community over the past four decades, condition monitoring and fault tolerant control are lacking efficiency due to the ever-increasing complexity of the systems.

Future work will aim to provide insights about how a CAS will show **robust, compliant, and intelligent physical interactions** with the environment, human beings, or other systems. In this regard, real-time, energy-efficient computing is required to advance the type of primitive collaborations that are achievable so far. With this aim, systems should be equipped with small processors able to ensure low energy consumption, and, at the same time, increase the memory bandwidth. Current alternatives (e.g., multicore central processing units—CPUs [560], new graphical processors—GPUs [561], parallel processing core—SpiNNaker [562]) still
<figure><img src="image_45.png" /><figcaption>**Figure 42.** Overall idea of a collaborative autonomous control system. The supervisor manages the entire system, observes and analyses the whole situation and provides information to each agent to improve their autonomous actions and optimize the operations.</figcaption></figure>

suffer from an extremely high energy demand that is not sustainable and they cannot be easily scaled. Additionally, a limited number of processes can run simultaneously, and the speed of the response is still low. Consequently, new neuromorphic architectures are the most promising alternatives to address the increasing demand to create CAS able of a seamless interaction with human beings.

### 30.3. Advances in science and technology to meet challenges

In this section, we discuss the foremost advances in science and technology that will address the main aforementioned challenges.

**Mission planning.** Novel AI-based heuristic methods will be developed to equip mission planners with key functionalities that will increase the value for the human operators. These include: the close-loop decomposition of missions to achieve an adaptive task allocation by leveraging information gathered at mission execution; automated survivability prediction to assess the likelihood of vehicle loss based on faults and failures occurred in past missions; automated reliability assessment to forecast the probability of mission failure based on past missions' information; automated learning from previous missions' performance to tune the future missions' parameters; inclusion of services to extend the mission endurance [563, 564].

**Fault-tolerant and cooperative control.** Paradigms based on cooperation will be created to fulfil the advances in multi-agent systems [559]. Cooperation among agents offers the possibility of achieving fault-tolerance towards sensors and actuators faults through the design of diagnostic solutions that leverage shared proprioceptive and exteroceptive information. Prescribe-time fault tolerant cooperative control solutions for safety critical cyber-physical systems will be achieved; these will provide the basis for efficient fault-tolerant algorithms able to trade-off between fast convergence and acceptable fault-tolerance performance.

**Robust, compliant and intelligent physical interactions.** New physical mechanisms will be designed to provide passive properties to the system, to increase the physical interaction performances, and include advanced control aspects for achieving simultaneous robustness and compliance. The advances in neuro robotics and neuromorphic computing will influence the development of the next generation of intelligent agents [565]. Current neuromorphic computing systems already exploit learning and adaptive skills in systems compared to conventional von Neumann machines thanks to non-volatile memories and power efficiency performance [566]. However, new types of sensors and actuators will be introduced to enhance the cognitive and learning functionalities of the systems and deal with safety and robustness concerns. Advanced bio-inspired platforms, e.g., brain-on-the-chip devices, will be designed for processing complex brain-inspired computing techniques that will support autonomy, more connectivity, increased decentralization, and high-performance computing. Indeed, neuromorphic technologies will be able to process complex unstructured data and learn to self-respond to external unknown stimuli enabling their use in critical edge applications, for example in autonomous navigation, human-machine interactions and smart healthcare markets.

Finally, innovative applications could be generated through the development of self-reconfigurable modular CAS, systems able to adapt their morphology and functionality to varied environments including unforeseen conditions [567]. This will require self-learning capabilities to develop new knowledge and to decide upon the previous accumulated experience.
### 30.4. Concluding remarks

This paper has presented the future perspectives of CAS and the main challenges and research issues that need to be addressed toward their realization. Further to these scientific and technological challenges, there are ethical, social, and legal issues when realising CAS, though these are beyond the scope of this article.

CAS working alongside humans have already been deployed and they support humans' work ensuring high productivity, speed, and accuracy [568]; they also relieve us of many heavy and time-consuming tasks and reduce the overall risk of collisions. CAS provide an economically viable entry-point to automation of processes, i.e., accelerated testing scenarios on products, environmental impacts. Fusion of fundamental and applied research in both technical and natural sciences will facilitate the development of new theoretical frameworks for the design of intelligent CAS. Multiple disciplines will be merged to pursue a systematic innovation within cyber-physical systems with variable level of autonomy and cooperation; the use of AI and Internet of Everything technologies future proofs the system to address changing market demands and expectations in several technological areas. Applications will be many and varied including, and not limited to, manufacturing, health care, inspection and maintenance, precision farming, autonomous marine operations, and education.
# Section 5. Ethics

## 31. The ethics of developing neuromorphic technology

Martin Ejsing Christensen<fnref n="1" /> and Sune Holm<fnref n="2" />

E-mail: mech@dketik.dk and suneh@ifro.ku.dk

Like the development of other forms of AI, the development of neuromorphic technology may raise a number of ethical questions [569, 570] (figure 43).

One issue concerns privacy and surveillance. The development of most forms of AI depends upon access to data, and as far as these data can be seen as private or personally identifiable, it raises a question about when it is (ethically) defensible to use such data. On the one hand, some argue that persons have a right to be let alone and exercise full control over information about themselves, so that any use of such data presupposes fully informed consent. On the other hand, others recognize the importance of privacy but argue that it may sometimes be outweighed by the fact that reliable applications for the good of everyone presuppose access to high quality representative data [571].

Another issue concerns opacity. Many forms of AI support decision making based on complex patterns extracted from huge data sets. Often, however, it will be impossible not only for the person who makes the final decision but also for the developer to know what the system's recommendations are based on and it is in this sense that it is said to be opaque. For some such opacity does not matter as long as there are independent ways of verifying that the system delivers an accurate result, but others argue that it is important that the system is explainable [572]. In this way, a tension is often created between accuracy and transparency, and what the right trade-off is may often depend upon the concrete context.

Opacity is closely connected with the question of bias since opacity may hide certain biases. There are different forms of bias but in general, bias arises when automated AI decision support systems are based on data that is not representative of all the individuals that the system supports decisions in relation to [573]. There are different opinions as to when the existence of bias in automated decision support systems poses a serious problem. Some argue that ‘traditional’ unsupported human decision-making is biased, too, and that the existence of bias in automated AI decision support systems only pose a serious problem if the bias is more significant than the pre-existing human bias. Others argue that features such as opacity or the lack of suitable institutional checks and balances may tend to make the existence of bias in automated decision support systems more problematic than ‘ordinary’ human bias [574]. A separate problem is created by the fact that it sometimes will be easier to identify and quantify bias in AI systems than in humans, making a direct comparison more difficult.

The development of forms of AI based on neuromorphic technology also raises questions about manipulation of human behavior, online as well as offline. One context in which such questions arise is advertising and political campaigning, where AI generated deep knowledge about individuals' preferences and beliefs, which may be used to influence them in a way that escapes the individuals' own awareness. Similar issues may also arise in connection with other forms of AI such as chatbots and care or sex robots that simulate certain forms of human behavior without being 'the real deal'. Even if persons develop some form of emotional attachment to such systems, some argue that there is something deeply problematic and deceptive about such systems [575], while others point out that there is nothing intrinsically wrong with such systems as long as they help satisfy human desires [576]. If, as described in section 4.1, neuromorphic technologies will make it possible for robots to move from extremely controlled environments to spaces where they collaborate with humans and exhibit continuous learning and adaptation, it may make such questions more pressing.

A distinct set of issues are raised by the possibility of developing AI systems that do not just support human decision making but operate in a more or less autonomous way such as ‘self-driving’ cars and autonomous weapons. One question that such systems raise concerns the way in which they should be programmed in order to make sure that they make ethically justifiable decisions (in most foreseeable situations). Another question concerns how responsibility and risk should be distributed in the complex social system they are a part of. If, as described in section 4.2, neuromorphic engineering offers the kind of technological leaps required for achieving truly autonomous vehicles, the development of neuromorphic technologies may make such questions more pressing than at present.

A distinct issue relates to sustainability. As pointed out in the introduction, 5%–15% of the world's energy is spent in some form of data manipulation (transmission or processing), and as long as a substantial amount of that energy comes from sources that contribute to climate change through the emission of greenhouse gases,

<footnote n="1">¹The Danish Council on Ethics</footnote>

<footnote n="2">²University of Copenhagen</footnote>
<figure><img src="image_46.png" /><figcaption>**Figure 43.** Some of the most salient ethical issues raised by the development of neuromorphic technology.</figcaption></figure>

it raises a question as to whether all that data manipulation is really necessary or could be done in a more energy efficient way. And in so far as neuromorphic technologies, as e.g. pointed out in section 28, shows a reduction of up to three orders of magnitude in terms of power consumption compared to state-of-the-art ML approaches, it seems to provide robust ethical support for the development of neuromorphic technologies.

As mentioned in the beginning of this section, the ethical questions raised by the development of neuromorphic technology is not unique to this technology but related to the development of AI as such. The successful development of neuromorphic technology may make some of the issues more pressing, and a central task for future work on the ethics of neuromorphic technology will, accordingly, be to inquire into the exact way in which the issues are raised by the development of neuromorphic technology. But the existing forms of AI already raise many of the questions described so far. Besides these questions, however, the development of neuromorphic technology (as well as other forms of AI) may also raise a number of questions that are more speculative either because it is unclear whether the development will take place, when it will happen or what the precise consequences will be.

One such issue has to do with automation and unemployment. AI systems have already replaced humans in certain job functions (e.g., customer service), but it has been suggested that most job functions will be affected by the development of AI at one point [577]. Because such a development has the potential to disrupt the social order (e.g., through mass unemployment) it raises an important ethical (and political) question as to how AI systems should be introduced into society [578].

Another more speculative issue relates to artificial moral agents and so-called robot rights. If the development of neuromorphic (and other) forms of AI leads to the creation of systems that possess some or all the traits that make us ascribe rights and responsibilities to humans, it may thus raise a question about whether such rights and responsibilities should be ascribed to artificially intelligent systems [579, 580].

Thirdly, some have also pointed out that the development of neuromorphic (and other) forms of AI may create issues related to the so-called singularity. The idea is that the technological development may lead to the creation of general forms of AI that surpass the human level of intelligence and then begin to control the further development of AI in ways that may not be in the interests of the human species and perhaps even threaten its very existence. Whether such a scenario is likely has been questioned [581], but some argue that even a slight risk should be taken serious given the potentially devastating consequences [582].

No matter what one thinks is the right answer to the ethical questions raised by the development of neuromorphic technology, it is, finally, worth noticing that it still leaves an important practical question: how best to make sure that the actual development and implementation of neuromorphic technology will take place in an ethically defensible way. For some questions, governmental regulation may be the best means. For others, the best solution may be to trust the community of developers to make the right, value-based decisions when designing systems, while some questions, perhaps, should be left to the enlightened citizenry. In
the end, however, it will probably be up to an inquiry into the concrete situation to decide when one or the other approach—or combination of approaches—provides the best means of securing an ethically defensible development of neuromorphic technology.

## Data availability statement

The data that support the findings of this study are available upon reasonable request from the authors.

## ORCID iDs

Dennis V Christensen https://orcid.org/0000-0003-0048-7595

Abu Sebastian https://orcid.org/0000-0001-5603-5243

Manuel Le Gallo https://orcid.org/0000-0003-1600-6151

Stefan Slesazeck https://orcid.org/0000-0002-0414-0321

Thomas Mikolajick https://orcid.org/0000-0003-3814-0378

Sabina Spiga https://orcid.org/0000-0001-7293-7503

Ilia Valov https://orcid.org/0000-0002-0728-7214

Gianluca Milano https://orcid.org/0000-0002-1983-6516

Carlo Ricciardi https://orcid.org/0000-0002-4703-7949

Shi-Jun Liang https://orcid.org/0000-0002-3466-8063

Feng Miao https://orcid.org/0000-0002-0962-5424

Mario Lanza https://orcid.org/0000-0003-4756-8632

Scott T Keene https://orcid.org/0000-0002-6635-670X

Alice Mizrahi https://orcid.org/0000-0003-2043-049X

J Joshua Yang https://orcid.org/0000-0001-8242-7531

Giacomo Indiveri https://orcid.org/0000-0002-7109-1689

John Paul Strachan https://orcid.org/0000-0002-5718-7924

Elisa Vianello https://orcid.org/0000-0002-8868-9951

Srikanth Ramaswamy https://orcid.org/0000-0001-6642-7136

Gabriella Panuccio https://orcid.org/0000-0002-4059-7193

Mufti Mahmud https://orcid.org/0000-0002-2037-8348

Elisa Donati https://orcid.org/0000-0002-8091-1298

Silvia Tolu https://orcid.org/0000-0003-1825-8440

Daniele Ielmini https://orcid.org/0000-0002-1853-1614

N Pryds https://orcid.org/0000-0002-5718-7924

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