Noisy Machines: Understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation
Authors: Chuteng Zhou
Prad Kadambi
Matthew Mattina
Paul N. Whatmough
Venue:  (2020)
Keywords: network noise robustness
analog accelerator
noise injection
distillation
error rate

Abstract: The success of deep learning has brought forth a wave of interest in computer hardware design to better meet the high demands of neural network inference. In particular, analog computing hardware has been heavily motivated specifically for accelerating neural networks, based on either electronic, optical or photonic devices, which may well achieve lower power consumption than conventional digital electronics. However, these proposed analog accelerators suffer from the intrinsic noise generated by their physical components, which makes it challenging to achieve high accuracy on deep neural networks. Hence, for successful deployment on analog accelerators, it is essential to be able to train deep neural networks to be robust to random continuous noise in the network weights, which is a somewhat new challenge in machine learning. In this paper, we advance the understanding of noisy neural networks. We outline how a noisy neural network has reduced learning capacity as a result of loss of mutual information between its input and output. To combat this, we propose using knowledge distillation combined with noise injection during training to achieve more noise robust networks, which is demonstrated experimentally across different networks and datasets, including ImageNet. Our method achieves models with as much as 2X greater noise tolerance compared with the previous best attempts, which is a significant step towards making analog hardware practical for deep learning.

Under review as a conference paper at ICLR 2020
NOISY MACHINES: UNDERSTANDING NOISY NEURAL NETWORKS AND ENHANCING ROBUSTNESS TO ANALOG HARDWARE ERRORS USING DISTILLATION
Anonymous authors Paper under double-blind review
ABSTRACT
The success deep learning has brought forth a wave of interest in computer hardware design better meet the high demands of neural network inference. In particular, analog computing hardware has been heavily motivated specifically for accelerating neural networks, based either electronic, optical or photonic de- vices, which may well achieve lower power consumption than conventional digital electronics. However, these proposed analog accelerators suffer from the intrin- sic noise generated by their physical components, which makes it challenging to achieve high accuracy on deep neural networks. Hence, for successful deploy- ment on analog accelerators, it is essential to be able to train deep neural net- works to be robust to random continuous noise the network weights, which is somewhat new challenge in machine learning. In this paper, we advance the understanding of noisy neural networks. We outline how a noisy neural network has reduced learning capacity as result of loss of mutual information between its input and output. To combat this, we propose using knowledge distillation com- bined with noise injection during training to achieve which is demonstrated experimentally across different networks and datasets, in- cluding ImageNet. Our method achieves models with as much noise tolerance compared with the previous best attempts, which is significant step towards making analog hardware practical for deep learning.
noise robust networks,
2x greater
INTRODUCTION
Deep neural networks (DNNs) have achieved unprecedented performance over wide variety of tasks such as computer vision, speech recognition, and natural language processing. However, DNN inference is typically very demanding in terms of compute and memory resources. Consequently, larger models are often not well suited for large-scale deployment on edge devices, which typically have meagre performance and power budgets, especially battery powered mobile and IoT devices. To address these issues, the design of specialized hardware for DNN inference has drawn great interest, and an extremely active area of research. To date, plethora of techniques have been proposed for designing efficient neural network hardware (Sze et al. (017))
In contrast the current status quo of predominantly digital hardware, there significant research interest in analog hardware for DNN inference. In this approach, digital values are represented by analog quantities such as electrical voltages or light pulses, and the computation itself (c.g., multiplication and addition) proceeds in the analog domain, before eventually being converted back to digital. Analog accelerators take advantage of particular efficiencies of analog computation in exchange for losing the bit-exact precision of digital. In other words, analog compute is cheap but somewhat imprecise. Analog computation has been demonstrated in the context of DNN inference in both electronic (Binas et al. 2016), photonic (Shen et al.) 2017) and optical (Lin et al. 2018) systems. Analog accelerators promise to deliver at least two orders of magnitude better performance over conventional digital processor for deep learning workloads in both speed (Shen et al.) 2017) and energy efficiency (Ni et al.) 2017). Electronic analog DNN accelerators are arguably the most mature technology and hence will be our focus in this work.

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The most common approach electronic analog DNN accelerator is in-memory computing, which typically uses non-volatee volatlla memory (NVM) crossbar arrays to encode the network weights as analog values. The NVM itself can be implemented with memristive devices, such metal-oxide resis- tive random-access memory (ReRAM) (Hu et al., 2018) or phase-change memory (PCM) (Le Gallo et al. 2018 Boybat al. 2018 Ambrogio al. 2018). The matrix-vector operations computed during inference are then performed in parallel inside the crossbar array, operating on analog quan- tities for weights and activations. For example, addition of two quantities encoded as electrical currents can be achieved by simply connecting the two wires together, whereby the currents will add linearly according to Kirchhoff's s current law. In this case, there is almost zero latency or energy dissipation for this operation.
Similarly, multiplication with a weight can be achieved by programming the NVM cell conduc- tance to the weight value, which is then used to convert an input activation encoded as voltage into a scaled current, following Ohm's law. Therefore, the analog approach promises significantly improved throughput and energy efficiency. However, the analog nature of the weights makes the compute noisy, which can limit inference accuracy. For example, a simple two-layer conne-connected network with baseline accuracy of 91.7% on digital hardware, achieves only 76.7% when imple- mented on an analog photonic array (Shen et al. (017). This kind of accuracy degradation is not acceptable for most deep learning applications. Therefore, the challenge of imprecise analog hard- ware motivates us to study and understand noisy neural networks, in order to maintain inference accuracy under noisy analog computation.
The question of how to effectively learn and compute with a noisy machine is long-standing prob- lem of interest in machine learning and computer science (Stevenson al. 1990 Von Neumann 1956). In this paper, we study noisy neural networks to understand their inference performance. We also demonstrate how train a neural network with distillation and noise injection to make it more resilient to computation noise, enabling higher inference accuracy for models deployed on analog hardware. We present empirical results that demonstrate state-of-the-art noise tolerance on multiple datasets, including ImageNet.
The remainder of the paper is organized as follows. Section 2 gives an overview of related work. DectionnJutline the problem statement. Section presents more formal analysis of noisy neural networks. Section gives a distillation methodology for training noisy neural networks, with exper- imental results. Finally, Section 6 provides brief discussion and Section closes with concluding remarks.
2 RELATED WORK
Previous work broadly falls under the following categories: studying the effect of analog computa- tion noise, analysis of noise-injection for DNNs, and use of distillation in model training.
Analog Computation Noise Models In Rekhi et al. (0019), the noise due to analog computa- tion is modeled as additive parameter noise with zero-mean Gaussian distribution. The variance of this Gaussian is function of the effective number of bits of the output of analog computation. Similarly, the authors in Joshi et al. (2019) also model analog computation noise as additive Gaus- sian noise the parameters, where the variance is proportional to the range of values that their PCM device can represent. Some noise models presented have included a more detailed account of device-level interactions, such as voltage drop across the analog array (Jain al.) 2018 |Feinberg) et al. 2018), but are beyond the scope of this paper. In this work, we consider an additive Gaussian noise model on the weights, similar Rekhi al.) (2019); Joshi et al. (2019) and present novel training method that outperforms the previous work in model noise resilience.
Noise Injection for Neural Networks Several stochastic regularization techniques based on noise-injection and dropout (Srivastava et al. 2014, Noh et al.) 2017 Li & Liu] (016) have been demonstrated to be highly effective at reducing overfitting. For generalized linear models, dropout and additive noise have been shown to be equivalent to adaptive L2 regularization to a first order WWgerr et al.) 2013). Training networks with Gaussian noise added to the weights activations can also increase robustness to variety of adversarial attacks (Rakin et al., 2018). Bayesian neural networks replace deterministic weights with distributions in order to optimize over the posterior
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distribution of the weights (Kingma & Welling 2013). Many of these methods noise injection at inference time to approximate weight distribution; in Gal Ghahramani (2016) a link between Gaussian processes and dropout is established in effort to model the uncertainty of the output of a network. A theoretical analysis by Stevenson et al. (190)) has shown that for neural networks with adaptive linear neurons, the probability of error of noisy neural network classifier with weight noise increases with the number of layers, but largely independent of the number of weights per neuron neurons per layer.
Distillation in Training Knowledge distillation (Hinton al., 2015) is well known technique in which the soft labels produced by a teacher model are used to train a student model which typ- ically has reduced capacity. Distillation has shown merit for improving model performance across a range of scenarios, including student models lacking access to portions of training data (Micaelli Storkey, 2019), quantized low-precision networks (Polino et al. 2018 Mishra Marr 2017). protection against adversarial attacks (Papernot et 2016 Goldblum et al. (019). and in avoiding catastrophic forgetting for multi-task learning (Schwarz et al. 2018). To the best of our knowledge, our work is the first to combine distillation with noise injection in training to enhance model noise robustness.
3
PROBLEM STATEMENT
1th Convolution Layer
Weight Matrix (W4)
Noisy Analog Conductances (G')
3x3 Kernel x 16 Input Ch.
16 Input Channels
W
(xx)
Flatten (IM22CL)
Noise Model, Aw
(xfxxe)
Output Channels
NVM Device
v
v(
Figure 1: Deploying a neural network layer, on an analog in in-memory crossbar involves first flat- tening the filters for a given layer into weight matrix W which is then programmed into an array
of NVM devices which provide differential conductances G for analog multiplication. random Gaussian A W' is used to model the inherent imprecision in analog computation.
Without loss generality, we model a general noisy machine after a simple memristive crossbar array, similar to Shafiee al. (2016). Figure illlusraate how arbitrary neural network layer, such as typical x 3 convolution, can be mapped to this hardware substrate by first flattening the weights into a single large 2D matrix, W and then programming each element of this matrix into a memristive cell in the crossbar array, which provides the required conductances G (the reciprocal of resistance) perform analog multiplication following Ohm's law, lout Vin G. Note that pair of differential pair of NVM devices are typically used to represent a signed quantity in G!. Subsequently, input activations, x' converted into continuous voltages, v(x'), ), are streamed into the array rows from the left-hand side. The memristive devices connect row with columns, where the row voltages are converted into currents scaled by the programmed conductance, G, to generate the currents ((y²). which are differential in order to represent both positive and negative quantites with unipolar signals. The currents from each memristive device essentially add up for free where they connected in the columns, according to Kirchhoff's current law. Finally, the differential currents are converted to bipolar voltages, v(y²), which are they digitized before adding bias, and performing batch normalization and ReLU operations, which not shown in Figure 1
However, the analog inference hardware of Figure i subject to real-world non-idealities, typically attributed to variations in: 1) manufacturing process, 2) supply voltage and 3) temperature, PVT variation collectively, all of which result in noise the system. Below we discuss the two key components in terms of analog noise modeling.
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Data Converters. Digital-to-an toatlooo-aoggga converter (DAC) and amalog-to-dightt converter (ADC) cir- cuits are designed to be robust to PVT variation, but in practice these effects do degrade the reso- lution (i.e. number of bits). Therefore, we consider effective number of bits (ENOB), which is lower bound on resolution in the presence of non-idealities. Hence, we use activation and weight quantization with ENOB data converters and additional converter noise modeling.
NVM cells. Due to their analog nature, memristive NVM cells have limited precision, due to the read and write circuitry (Joshi et al., 2019). between write and read operations, their stored value is prone to drift over time. Long-tern drift can be corrected with periodic refresh operations. At shorter timescales, time-varying noise may be encountered. For most of the experiments in this paper, model generic NVM cell noise the weights of the model in each particular layer AW ~ ((WW 0, R..). This simple model, described more concretely in Section 5 is similar to that used by Joshi et al. (2019) which was verified on real hardware. In addition, also investigate apatially-varyiee and varying noise models in Section 5.2 (Table 1
additive zero-mean i.i.d Gaussian error term on
4 ANALYSIS OF NOISY NEURAL NETWORKS
4.1 BIAS VARIANCE DECOMPOSITION FOR NOISY WEIGHTS
Naively deploying an off-the-shelf pretrained model on a noisy accelerator will yield poor accuracy for fundamental reason. Consider neural network f(W; with weights W that maps input x E R" to an output y E R. In the framework statistical learning, æ and are considered to be randomly distributed following joint probability distribution p( y). In noisy neural network, the weights W are also randomly distributed, with distribution p( ()) The expected Mean Squared Error (MSE) of this noisy neural network can be decomposed as
(( W (/wppp/W[WWWWW>:> =))
-E(av)sp(x.)) W-ppW)/(/((W))) ([W;: (W x]] + (w y) -Eeppla) (WW - L
+ +E(ee)))p(a..)
[ [EW>ptt) ([W; - 3)
(1)
The first term on the right hand side of Equation [ is a variance loss term due to randomness in the weights and is denoted as har- The second term is squared bias loss term which we call bias- However, typically a model is trained to minimize the empirical version of expected loss ppretrained = ((x,))>((x.)) [[f W| =)) - ") We assume that the noise is centered such that pretrained weights are equal to E W]. A pretrained model is therefore optimized for the wrong loss function when deployed on a noisy accelcrator. To show this in a more concrete way, a baseline LeNet model (32 filters in the first convolutional layer, 64 filters in the second convolutional layer and 1024 neurons in the fully-connected layer) (LeCun et al.) 1998) is trained on MNIST dataset to 99. 19% accuracy and then exposed Gaussian noise in weights, numerical values of these loss terms can be estimated. The expected value of the network output [w[ æ)] is estimated by averaging over outputs of different instances of the network for the same input æ. We perform inference on n = 100 different instances of the network and estimate the loss terms as
7(W: .)) = Ew (WW) [=)
Σ f(W;z))
(2)
1
f.ur
(f(W; - (W:z,)))
(3)
N n
1 N
Prins
([(: -5) N
(4)
N
ppretrained
Σ
(JE xz)) - -2
(5)
N
j=1
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The above formulas for network with scalar output. They be easily extended to the vector output case by averaging over all outputs. In the LeNet example, we take the output of softmax layer to calculate squared losses. The noise is assumed i..d. Gaussian centered around zero with /
fixed SNR w in each layer I. The numerical values of the above losses are estimated using the entire test dataset for different noise levels. Results are shown Figure a). Iiias initially equal (pretrained and (var = when there is no noise. However, as noise level rises, they increase in magnitude and become much more important than ppretrained- lvar overtakes Iiias to become the predominant loss term in a noisy LeNet at aN = 0.6. It is useful to note that hias increases with noise entirely due to nonlinearity in the network, which is ReLU in the case of LeNet. In
x)) = L[f(W;; =)] A linear model, bias should be equal to 'pretrained as we would have [E[ model trained in conventional manner thus not optimized for the real loss it is going to encounter on a noisy accelerator. Special retraining required to improve its noise tolerance. In Figure 2 a), show how the model accuracy degrades with rising noise level for the baseline LeNet and its deeper and wider variants. The deeper network is obtained by stacking two more convolutional layers of width 16 in front of the baseline network and the wider network is obtained by increasing the widths of each layer in the baseline to 128, 256, 2048 respectively. Performance degradation due noise is worse for the deeper variant and less severe for the wider one. A more detailed discussion of the network architecture effect on its performance under noise is offered in Section 4.2
a)
30
Lass d decomposition for naisy LeNet
b)
10: Y) decay in noisy LeNet and variants
1.0
12 64 1024 (faseline)
Normalizee MSE en Test Set
25
90
16- 16 16-126
MNsTT Test Accuraccy(
0.8
128 - 256 2048
20
Accuracy (Baseline)
Accuracy (Deep)
Normalized Mutual
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Accuracy (Wide)
70
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ou/ow
Figure 2: (a) Different loss terms on the test dataset and model test accuracy as a function of noise standard deviation, the losses are normalized to the pretrained model loss Ipretrained calculated using clean weights. Accuracy is calculated by performing the inference 100 times on the test set, error bars show the standard deviation. Estimate of normalized mutual information between the input and output of the bascline LeNet and its variants as a function of noise standard deviation. A random subset of 200 training images are used for this estimate, with each inference repeated 100 times random realization of the network to estimate H X.. Mutual information decays with rising noise, deeper and narrower networks are more susceptible to this decay.
4.2 LOSS OF INFORMATION IN A NOISY NEURAL NETWORK
Information theory offers useful tools to study noise in neural networks. Mutual information Y( characterizes the amount of information obtained random variable X by observing another random variable Y. The mutual information between X and Y can related to Shannon entropy by
I(X:Y) = H(Y) X)
Mutual information has been used to understand DNNs (Tihhyy & Zaslavsky 2015 Saxe et al., 2018). Treating noisy neural network as noisy information channel, we can show how informa- tion about the input to the neural network diminishes as propagates through the noisy computation. In this subsection, X is the input to the neural network and Y is the output. Mutual information is estimated for the baseline LeNet model and its variants using Equation 6, When there no noise, the term HY is zero as Y is deterministic once the input to the network X is known, therefore [(X; Y) is just (Y) in this case. Shannon entropy (Y) can be estimated using a standard dis- crete binning approach (Saxe al. 2018). In our experiment, Y is the output of the softmax layer
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which is vector of length 10. Entropy H() is estimated using four bins per coordinate of Y by
(Y) E Ps log(p:),
where p; is the probability that output falls in the bin i. When noise is introduced to the weights, the conditional entropy H (Y (X) is estimated by fixing the input X = x and performing multiple noisy inferences to calculate #( = x) with the above binning approach. [ = x) is then averaged over different input to obtain X)).. This estimate is performed for LeNet and its variants with different noise levels. Results are shown in Vigure[(b) The values are normalized to the estimate of T(X; Y) at zero noise. Mutual information between the input and the output decays towards zero with increasing noise in network weights. Furthermore, mutual information in a deeper and narrower network decays faster than in shallower and wider network. Intuitively, information from the input undergoes more noisy compute when more layers are added to the network, while a wider network has more redundant paths for the information to flow, thus better preserving it. An information theoretic bound of mutual information decay as a function of network depth and width in a noisy neural network will be treated in our follow-up work. Overall, noise is damaging the learning capacity of the network. When the output of the model contains no information from its input, the network loses all ability to learn. For noise level that is not so extreme, a significant amount of mutual information remains, which indicates that useful learning is possible even with a noisy model.
COMBINING NOISE INJECTION AND KNOWLEDGE DISTILLATION
5.1 METHODOLOGY
Noise injection during training is one way of exposing network training to more realistic loss as randomly perturbing weights simulates what happens in a real noisy analog device, and forces the network to adapt noise during training. Noise injection only happens in training during forward propagation, which can be considered as an approximation for calculating weight gradients with a werohtttttoouuh--ttitttt
(STE) (Bengio et al. layer is drawn from an i.i.d. Gaussian distribution A(W', W 2 (I).. The noise is referenced to the range of representable weights W max
2013). At each forward pass, the weight W of
W min in that particular layer
NN = nWW max - )
(8)
where 17 a coefficient characterizing the noise level. During back propagation, gradients are calcu- lated with clean weights W and only W gets updated by applying the gradient. W and W are hyperparameters which can be chosen with information on the weight distributions.
min
Knowledge distillation was introduced by Hinton et al. (2015) as way for training smaller student model using a larger model as the teacher. For an input to the neural network æ, the teacher model generates logits z!", which are then turned into a probability vector by the softmax layer
exp(=7 IT)
o.- = o= E,
E, exp(22//T)
The temperature, T, controls the softness of the probabilities. The teacher network can generate softer labels for the student network by raising the temperature T. We propose to use a noise free clean model as the teacher to train a noisy student network. The student network is trained with noise injection to match mix of hard targets and soft targets generated by the teacher. Logits generated by the student network are denoted as 25. A loss function with distillation for the student model can be written
(10) Here H. is cross-entropy loss, Itrue is the one-hot encoding of the ground truth, and R is the Ly- regularization term. Parameter balances relative strength between hard and soft targets. We follow the original implementation in Hinton et al. (2015), which includes a 2 factor in front of the soft target loss to balance gradients generated from different targets. The student model is then trained
L(x: S T) H(00(28: = 1), (true) (=))
R(W.).

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with Gaussian noise injection using this distillation loss function. The vanilla noise injection training corresponds to the case where a = 0. If the range of weights is not constrained and the noise reference is fixed, the network soon learns that the most effective way to decrease the loss is to increase the amplitude of the weights, which increases the effective SNR. There are two possible ways to deal with this problem. Firstly, the noise reference could be re-calculated after each weight update, thus updating the noise power. Secondly, we can constrain the range of weights by clipping them to the range min> W magg, and use fixed noise model during training. We found that in general the second method of fixing the range of weights and training for a specific noise yields more stable training and better results. Therefore, this is the training method that we adopt in this paper. A schematic of our proposed method is shown in Figure the Appendix.
During training, a clean model first trained to its full accuracy and then weight clipping is applied to clip weights in the range [Wa min: W m The specific range is chosen based on statistics of the weights. Fine-tuning then applied to bring the weight-clipped clean model back to full accuracy. This model is then used the teacher to generate soft targets. The noisy student network is ini- tialized with the same weights as the teacher. This can be considered as a warm start to accelerate retraining. As we discussed earlier, the range of weights is fixed during training, and the noise injected into the student model is referenced to this range.
Our method also supports training for low precision noisy models. Quantization reflects finite preci- sion conversion between analog and digital domains in an analog accelerator. Weights are uniformly quantized in the range [W min: W max] before being exposed noise. In given layer, the input tivations are quantized before being multiplied by noisy weights. The output results of the matrix multiplication are also quantized before adding biases and performing batch normalization, which are considered to happen in digital domain. When training with quantization, the straight-through- estimator is assumed when calculating gradients with back propagation.
5.2 EXPERIMENTAL RESULTS
In order to establish the effectiveness of our proposed method, experiments are performed for dif- ferent networks and datasets. In this section we mainly focus on bigger datasets and models, while results on LeNet and its variants with some discussion of network architecture effect can be found in Figure of the Appendix. ResNets are family of convolutional neural networks proposed by He al. (2016). which have gained great popularity in computer vision applications. In fact, many other deep neural networks also use ResNet-like cells as their building blocks. ResNets are often used as industry standard benchmark models to test hardware performance. The first set of experiments we present consist of a ResNet-32 model trained on the CIFAR 10 dataset. In order to compare fairly with the previous work, we follow the implementation in Joshi et al. (2019), and consider a ResNet- 32(v1) model on CIFARIO with weight clipping in the range | 2o.,, 20w,). The teacher model is trained to an accuracy of 93. 83.845% using stochastic gradient descent with cosine learning rate de- cay (Loshchiloy & Hutter 2016), and an initial learning rate of 0.1 (batch size is 128). The network is then retrained with noise injection to make it robust against noise. Retraining takes place for 150 epochs, the initial learning rate is 0.01 and decays with the same cosine profile. We performed two sets of retraining, one without distillation in the loss (a = 0), and another with distillation loss (a 1). Everything else was kept equal in these retraining runs. Five different noise levels are tested with five different values of ry: {0.02, 0.04, 0.057, 0.073, 0.113.
Results are shown in Figure 3 a)). Every retraining run was performed twice and inference was performed 50 times on the test dataset for one model, to generate statistically significant results. Temperature was set to T 6 for the runs with distillation. We found that an intermediate tempera- ture between 2 and 10 produces better results. The pretrained model without any retraining performs very poorly at inference time when noise is present. Retraining with Gaussian noise injection can effectively recover some accuracy, which we confirm as reported in Joshi et al. (2019). Our method of combining noise injection with knowledge distillation from the clean model further improves noise resilience by about 40% in terms of 17 which is improvement of almost 2x in terms of noise power oy--
The actual noise level in given device can only be estimated, and will vary from one device to another and even fluctuate depending on the physical environment in which it operates (Section 3 Therefore, it is important that any method enhance noise robustness can tolerate range of noise
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06.0
32 (PP32) CIFARLLO
RusNet-32 at different inference noise levels
CIAR10 Test Accuracy (09
03.5
a)
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b)
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Notee injection and distiliaion T-6, Gr 0.11
0.
00
0.92
0.04
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0.12
0.13 0.34
Figure 3: (a) Test accuracy as a function of noise level, here we have 7hr Tinf error bars show the standard deviation of different training and inference runs. Our method with distillation achieves the best robustness. (b) Comparison model performance noise levels different from the training level.
levels. Our method offers improved noise robustness, even when the actual noise at inference time different from that injected at training time. shown in Figure 3)b) that the model obtained from distillation is more accurate and less sensitive to noise level differences between training and inference time. This holds for range different inference noise levels around the training level. In the previous experiments, hardware, there could additional non-idcalities such as variation in noise level due to temperature fluctuation and nonuniform noise profile on different NVM cells due to statistical variation in the manufacturing process. We have conducted additional experiments to account for these effects.
assume fixed noise level parameterized by 17. On real analog
Results from the experiments variation over time. Noise 1) is randomly sampled from A ((E: no, o for each inference batch. A noise temporal fluctuation level of 10% means that on = 0.1 Spatial noise level fluctuation introduces nonuniform diagonal terms in the noise covariance matrix. More concretely, each weight noise in our previous model is multiplied by a scale factor Aw with A drawn from a Gaussian distribution ((w 1, x)). A noise spatial fluctuation level of 10% means that σ 0.1. The scale factors are generated and then fixed when the network is instantiated, therefore the noise during network inference non i.i.d. in this case. Results from our experiments show that there is no significant deviation when a combination of these non-ideal noise effects are taken into account.
shown in Table [ Temporal fluctuation represents noise level
Table 1: ResNet-32 on CIFARIO with analog non-idealities: our method of combining distillation and noise injection consistently achieves the best accuracy under different analog non-ideal effects.
Noise level
0.05
Temporal 10%
Temporal 20%
Temporal 0%
Temporal 0%
Temporal 20%
Non-ideal fluctuation type
Spatial
Spatial
Spatial 10%
Spatial 20%
Spatial 20%
93%
92.98%
92.98%
92.95%
92.94%
retraining
+/- 0.14%
+/- 0.18%
+/- 0.15%
+/- 0.15%
+/- 0.15%
93.18%
93.03%
93.1%
93.15%
93.11%
Noise injection
+/- 0.13%
+/- 0.15%
0.14%
+/- 0.15%
+/- 0.13%
93.56
93.55%
93.55%
93.51
93.53%
Distillation and noise injection
+/- 0.12%
+/- 0.11
0.13%
+/- 0.12%
0.12%
Noise level
Y)= 0.1
90.46%
90,22%
90.5%
90.4%
90.1
retraining
+/- 0.19%
+/- 0,27%
+/- 0.2%
+/- 0,23%
+/- 0,3%
91.87%
91.93%
91.91%
91.79%
91.81%
Noise injection
0.17%
0.2%
+/- 0.2%
0.18%
0.17%
92.83%
92.77%
92.88%
92.89%
92.86 %
Distillation and noise injection
+/- 0.18
+/- 0.14%
+/- 0.14%
+/- 0.14%
+/- 0.15%
The performance of our training method is also validated with quantization. A ResNet- model trained with quantization to 4-bit precision (ENOB) for both weights and activations. This cor- responds to 4-bit precision conversions between digital and analog domains. A subset of training
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data is passed through the full precision model calibrate the range for quantization the 0.1% and 99.9% percentiles as quin and Qmax for the quantizer. This range of quantization is fixed throughout training. The quantized model achieves an accuracy of 92.91% on the test dataset when no noise is present. The model is then re-trained for noise robustness. The noise level is ref- erenced the range of quantization of weights in one particular layer, such that W min Imin, and W Waaxx 0max,1 Results are shown for the same of 7) values in Figure aa). In the distillation retraining runs, the full-precision clean model with an accuracy of 93.87% is used as the teacher and temperature set to T Due to extra loss in precision imposed by aggressive quantization, accu- racy of the pretrained quantized model drops sharply with noise. At 17 0.057. the model accuracy drops to 87.5% without retraining and further down 80.9% at 17 0.073. Even retraining with noise injection struggles, and the model retrained with only noise injection achieves an accuracy of 90.34% at 1) = 0.073. Our method of combining noise injection and distillation stands out by keeping the accuracy loss within 1 % from the baseline up to noise level of 1) = 0.07.
choose
Quantized ResNet-118 16-bit Weight and Artovation) on CIFAR10
Restiet-1 B (6-2et Weight and Activation) retraining nake
93-
CIFAR10 Test Accuracy acy (90
92.5
92
90.0
b)
CIFAR10 Ascuracy
91
87.5
85.0
90
82.5
89
Baseline
80.0
Gaussian noise Injection
77.5
Noke ruection (ty 0.557 03
Wize nuctior Coy 9.85, -pr09.05))
Nosse injection and distilation T-6 (ours)
Mize nuctionn and dis larion (p- 6.057 d- -01
g
75.0
Mike npectoon and diolie (p.- 6.052 fer 0.057)
0.02
0.04
0.06
0.08
0.10
0.12
20
60
80
100
Opr frr
Epachs
Figure 4: (a) Test accuracy as a function of noise level for 4-bit ResNet- 18, here we have f)r Tha error bars show the standard deviation of different training and inference runs. Retraining with distillation and noise injection achieves the best results with quantization. (b) Test accuracy of different models during retraining with noise level 7) = 0.057.
One interesting aspect of using distillation loss during retraining with noise can be seen in Figure Ab.. The evolution of model accuracy on the test dataset is shown. When distillation loss is used, the model suffers an accuracy drop (difference between blue and orange curves) around 2.08% when tested with noise. The drop (difference between green and red curves) is significantly reduced to around 0.6% when distillation loss is used. This observation indicates that training with distillation favors solutions that are less sensitive to noise. The final model obtained with distillation is actually slightly worse when there is no noise at inference time but becomes superior when noise present.
Results on the ImageNet dataset for ResNet-50(y1) network are shown in Table 2 to demonstrate that our proposed approach scales to a large-scale dataset and a deep model. ResNet-50 model is first trained to accuracy of 74.942% with weight clipping in the range [-2o..1. 2ow.). This range is fixed as the reference for added noise. For ResNet-50 on ImageNet, only three different noise levels are explored, and the accuracy degrades very quickly beyond the noise level 17 0.06, as the model and the task are considerably more complex. Retraining runs for 30 epochs with an initial learning rate of 01.01 and cosine learning rate decay with batch size of 32. For distillation, we used a = and T = 6 as in previous experiments. Results are collected for two independent training runs in each setting and 50 inference runs over the entire test dataset. The findings confirm that training with distillation and noise injection consistently delivers more noise robust models. The accuracy uplift benefit also markedly increases with noise.
DISCUSSION
Effects of distillation Knowledge distillation is proven technique to transfer knowledge from larger teacher model to smaller, lower capacity student model. This paper shows, for the first time, that distillation is also effective way transfer knowledge between clean model and its noisy

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Under review as a conference paper at ICLR 2020
Table 2: ResNet-50 on ImageNet at different noise levels, showing the Top-1 accuracy on the test dataset, with no quantization applied. Uncertainty is the standard deviation of different training and inference runs.
Noise level
Training method
7=
77 = 0.02
? 0.04
1) = 0.06
72.975%
64.382%
46.284%
No retraining
74.942%
+/- 0.095%
0.121
+/- 0.179%
73.513%
70.142%
65.285%
Gaussian noise injection
74.942%
+/- 0.091%
+/- O. 0.129%
+/- 0.168%
74.005 %
71.442%
67.525%
Distillation and noise injection
74.942%
+/- 0.096%
+/- 0.111%
0.162%
counterpart, with the novel approach of combining distillation with noise injection during training. We give some intuition for understanding this effect with the help of Section (4.2 noisy neural network can be viewed as a model with reduced learning capacity by the loss of mutual information argument. Distillation is therefore acting to help reduce this capacity gap.
In experiments, distillation shows great benefit in helping the network to converge to good solution, even with high level of noise injected in the forward propagation step. Here, we attempt explain this effect by the reduced sensitivity of distillation loss. An influential work by Papernot et al. (2016) shows that distillation can be used to reduce the model sensitivity with respect to its input perturbations thus defending against some adversarial attacks. We argue that distillation can achieve similar effect for the weights of the network. Taking the derivative of the i-th output of the student network of at temperature T with respect to a weight w yields
0q. ow
exp(z./T)
Σ exp((2//T)
Oza Ow
0zj Ow
(11)
T T(E, (x,exp((TTT))
The 1/T scaling makes the output less sensitive to weight perturbation higher temperature, thus potentially stabilizing the training when noise is injected into weights during forward propagation. We plan to work on more formal analysis of this argument in our future work.
Hardware Performance Benefits The improvements in noise tolerance of neural networks demonstrated in this work have potential impact on the design of practical analog hardware celerators for neural network inference. Increased robustness to noisy computation at the model training level potentially means that the specification of the analog hardware can be relaxed. In turn, this can make it easier to achieve the hardware specification, or even allow optimizations to further reduce the energy consumption. An in-depth discussion of the trade-off between compute noise performance and hardware energy dissipation is beyond the scope of this paper, but we refer the interested reader to Rekhi et al. (2019) for more details. In summary, we believe that machine learning research will be key enabler for practical analog hardware accelerators.
CONCLUSION
Analog hardware holds the potential significantly reduce the latency and energy consumption of neural network inference. However, analog hardware is imprecise and introduces noise during com- putation that limits accuracy in practice. This paper explored the training of noisy neural networks, which suffer from reduced capacity leading to accuracy loss. We propose training methodology that trains neural networks via distillation and noise injection to increase the accuracy of models under noisy computation. Experimental results across range of models and datasets, including ImageNet, demonstrate that this approach can almost double the network noise tolerance compared with the previous best reported values, without any changes to the model itself beyond the training method. With these improvements in the accuracy of noisy neural networks, we hope to enable the implementation of analog inference hardware in the near future.
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A APPENDIX
C( W H(o(=f:T 1), Mtse)
+ of H(r(") +
Soft bobess
Teacher
TemporatureT
Distillation
H(a(2)TT).0
xewiios
Terpeectuuent
Output
Student
Hard Labels
Tomppornure-
Noise injection at forward pass only
entropy
Hio T' 1), Marue)
Figure 5: Schematic of our retraining method combining distillation and noise injection.
weight
INNIST Test Accuracy (96)
09
98
98
97
97
96
MNIST
Noise injection
Noise injection
Noise injection and distilatioon (ours)
Noise inpection and distillatoon (ours)
retraining
94
0.0
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0.3
0.4
94
0.0
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Tia
desper
MNIST Test Accuracy (96)
59
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98
97
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MNIST
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Noise Injection
Noise injection and distilatioon (ours)
inpection distilition (ours)
retraininng
retraining
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Figure 6: Results on LeNet and its variants show that our method combining distillation and noise injection improves noise robustness for different model architectures MNIST. The benefit of our method is the most significant when the network struggles to learn with vanilla noise injection retraining method. This threshold noise level depends the network architecture, have remarked for mutual information decay.
13


Reviews:
[{"review_id": "Syg_H-lOKB", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "* Summary *\nThe article on \"Noisy Machines\" addresses the issue of implementing deep neural network inference on a noisy hardware computing substrate, e.g. analog accelerators. This is an important topic because analog devices allow fast and energy efficient inference, which is crucial for inference at the edge. Because of their analog nature such devices suffer from noisy computations, and in this article the case of noisy weights is studied. \n\nThe main contributions of this article are the following:\n- an analysis of the performance loss in noisy networks by means of information theory and empirical results\n- the idea of combining noise injection during training with knowledge distillation\n- experimental evidence for a LeNet5 on MNIST, CIFAR10, and ResNet-50 on ImageNet\n\nIt has been shown in the literature that noise injection during training is an effective way to increase the noise robustness of neural networks. Relevant literature in this domain is cited. The novelty of the approach is to combine noise injection with distillation, by using the noise-free network as a teacher for the noisy network, which is initialized with the weights of the teacher. This is a novel variant of distillation and sounds like a simple to implement trick with beneficial results for increasing noise resiliency of networks. It is also proposed and shown that the method works for quantized networks.\n\nThe experimental results show that the combination of distillation and noise injection outperforms pure noise injection on all networks, as well as noisy inference without retraining. The effect is even more pronounced for quantized networks.\n\n* Evaluation *\nOverall I like this paper and think it is suitable to accept for ICLR, because it addresses an important practical problem of implementing deep networks on efficient hardware. The paper is well written and simple to understand and should be easy to implement (it would really help here providing code for the examples though). To the best of my knowledge I have not seen precisely this combination of noise injection and distillation, although there is a lot of literature about each individual approach. I appreciate that the authors made an effort to not just show empirical results but also motivate their findings by theory, although the argumentation stays a bit superficial.\n\nWhat I am mainly missing are two points:\n1. The assumed noise model of i.i.D. Gaussian weights is the simplest possible, and might deviate quite a bit in actual analog hardware. I would have liked to see a noise model that is derived from actual hardware observations, or maybe even a prototype implementation in hardware, such as was done e.g. in Binas et al. 2016. At the very least I would suggest to test the model on other noise models, including temporally changing noise levels, which could be a realistic scenario due to temperature fluctuations or other events.\n\n2. The experimental results focus on MNIST, CIFAR10, and later briefly on ImageNet. While the results are quite convincing on MNIST and CIFAR, these are easier datasets with usually well separable classes, so the effect of noisy inference might not be as pronounced, as in datasets with more confusion even in the clean case. In the case of ImageNet (Table 1) it looks like the difference to pure noise injection is not as big as it was in the CIFAR case, but here also only lower noise levels were tested. I would recommend testing also the same noise range as for CIFAR to understand whether distillation always shows the desired benefits, or if this is a diminishing effect for larger networks. Overall it would help to understand how the effect scales with network depth, e.g. by comparing the information loss for different ResNet depths.\n\nI'm giving weak accept and would change to accept if there could be clarification on how the approach scales to different network architectures and noise models closer to actual hardware. I also recommend publishing some example code for this approach.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "SJxtkoSRKS", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The authors of the manuscript study how inherent noise in the analog neural networks affects its accuracy. This is a very important topic as neural network based inference becomes uobiqutous and is required to run with very low power consumption and latency.\n\nThe manuscipt considers a system where the values of the neural network weights and biases are experiencing i.i.d Gaussian noise, which is a pretty good assumptions. However, in heavy use the system may warm up, and then there could be an effect that is correlated accross different weights. The noise model used would not be able to ensure proper inference in these conditions. I would like to see a discussion on the effect of correlations in the injected noise.\n\nThe mutual information is considered and evaluated for a the \"noisy\" and \"clean\" versions and the result is according to expectations. The some degree, I do not see this part very valuable, as it does not bring any particular insights on the analog neural network operation. Rather I woudl like to see how, the analog performance under noise scales as the neural has  more layers.  Also, the noise behavior of an analog RNN woudl be very interesting.\n\nThe authors have detected that even, if the neural network trainded without noise is not robust when the weigths fluctuate, the trained network is a good starting point for transfer learning. To some degree I do not find this to be a very inventive step as transfer learning has shown to able to cross much larger training data set alterations.\n\nGood solid work, but lacking non-obvious results, and I do not see manuscript adressing the the harder challenges. However, the quantified results may have a notable practical importance.\n\n\n", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "Bkl8CPGrjH", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "I started reading the paper with high hopes. The abstract and the introduction were set up nicely and I was quite intrigued to see a theoretical analysis and practical implementation of a neural network using analogue hardware. However, as I read through the paper carefully multiple times, I realized that the expository opening description fails to live up to the standard it sets. \n\nTo be more specific, I did not enjoy the elaborate description of noisy analogue conductances as the noise models analyzed in the paper are not tested on any of such devices. The noise model introduced is fairly simplistic and arguably, the real-world systems are much more complex compared to such simplistic assumptions. The authors could have presented the paper as an analysis of knowledge distillation in neural network training. Even if the paper were presented that way, I would have doubted its chance of acceptance due to the incrementality of the theoretical contribution.\n\nAll in all, I believe this is a very promising direction to invest, but the paper is not quite ready for ICLR.", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "BJgHvakR5S", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The manuscript illustrates how a noisy neural network can reduce the learning capacity. To mitigate this loss, the authors propose a method that combines the method of \"noise injection and \"knowledge distillation\". However, from a conceptual point of view,  their contribution (i.e. (10) in Section 5,) is unclear to me. Specifically,  the authors are not precise about how do they merge the aforementioned previous ideas and come up with the new loss function (10). \n\nMinor comment: Please correct (7).\n\n\n", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I did not assess the derivations or theory.", "review_assessment:_checking_correctness_of_experiments": "I did not assess the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I made a quick assessment of this paper."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper argues that NNs deployed to hardware needs to robust to additive noise and introduces two methods to achieve this.

The reviewers liked aspects of the paper and the paper is borderline. However, all in all sufficient reservations were raised to put the paper below the threshold. The criticism was constructive and can be used in an updated version submitted to next conference.

Rejection is recommended.
Decision: Reject