Query: Neuromorphic Photonics

title: Training Deep Neural Networks in Situ with Neuromorphic Photonics
abstract: We discuss an optoelectric architecture for executing the direct feedback alignment algorithm for neural network training. Using 1000 microring resonators, we can theoretically obtain speeds of 9.95 tera operations per second.

title: Femtojoule Technology Roadmap for TeraMAC Neuromorphic Photonic Accelerators
abstract: We analyze how neuromorphic photonic accelerators can provide energy efficiencies of a few fJ/MAC and footprint efficiencies in TeraMAC/s/mm2 regime to surpass both analog and digital electronic alternatives, by exploiting foundry ready photonic devices, yet without excluding lasers.

title: Neuromorphic Object Edge Detection with Artifical Photonic Spiking VCSEL Neurons
abstract: Fast spiking Vertical Cavity Surface Emitting Laser neurons are used to reveal neuromorphic object edge information in digital images. Input integration and thresholding are used in tandem with convolution to create a photonic spiking node with high prospects for convolutional neural networks and computer vision.

title: Silicon photonics for artificial intelligence applications
abstract: Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles) Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.

title: Reconfigurable all optical nonlinear activation functions
abstract: We experimentally demonstrate all optical reconfigurable nonlinear activation functions in a cavity loaded machzehnder interferometer device utilizing free carrier dispersion effect in silicon. Our device can be flexibly programmed to generate the following functions: clamped ReLU, sigmoid, radial basis and softplus, which can be employed in various neuromorphic tasks. nonlinear optics, neuromorphic photonics

title: Neuromorphic photonics for real time applications
abstract: This presentation will discuss demonstrations of real time operation of photonic neural networks going beyond machine learning applications. It contains a review of how a wavelength based neuron can be implemented using silicon photonics. We discuss three application examples: principal component analysis for blind source separation, fiber nonlinearity compensation in long haul communication systems and ODE system solving in real time.

title: Primer on silicon neuromorphic photonic processors: architecture and compiler
abstract: Microelectronic computers have encountered challenges in meeting all of today's demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing.

title: Opportunities for integrated photonic neural networks
abstract: Photonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Calculating the synaptic function, thereof, is computationally very expensive and does not scale well on state of the art computing platforms. Analog signal processing, using linear and nonlinear properties of integrated optical devices, offers a path toward substantially improving performance and power efficiency of these artificial intelligence workloads. The ability of integrated photonics to operate at very high speeds opens opportunities for time critical real time applications, while chip level integration paves the way to cost effective manufacturing and assembly.

title: MEMTONIC: A Neuromorphic Accelerator for Energy Efficient Deep Learning
abstract: Most deep learning accelerators in the literature focus only on improving the design of inference phase. We propose a novel photonics based backpropagation accelerator for high performance deep learning training. The proposed MEMTONIC architecture is a first of its kind memristor integrated photonics based deep learning architecture for end to end training and prediction. We evaluate the architecture using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG Net. The proposed design achieves at least 35x acceleration in training time, 31x improvement in computational efficiency, and 45x energy savings compared to the state of the art designs, without any loss of accuracy.

title: A Design Methodology for Post Moore's Law Accelerators: The Case of a Photonic Neuromorphic Processor
abstract: Over the past decade alternative technologies have gained momentum as conventional digital electronics continue to approach their limitations, due to the end of Moore's Law and Dennard Scaling. At the same time, we are facing new application challenges such as those due to the enormous increase in data. The attention, has therefore, shifted from homogeneous computing to specialized heterogeneous solutions. As an example, brain inspired computing has re emerged as a viable solution for many applications. Such new processors, however, have widened the abstraction gamut from device level to applications. Therefore, efficient abstractions that can provide vertical design flow tools for such technologies became critical. Photonics in general, and neuromorphic photonics in particular, are among the promising alternatives to electronics. While the arsenal of device level toolbox for photonics, and high level neural network platforms are rapidly expanding, there has not been much work to bridge this gap. Here, we present a design methodology to mitigate this problem by extending high level hardware agnostic neural network design tools with functional and performance models of photonic components. In this paper we detail this tool and methodology by using design examples and associated results. We show that adopting this approach enables designers to efficiently navigate the design space and devise hardware aware systems with alternative technologies.