Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and setting the firing threshold for each layer as the maximum input received in that layer. These type of converted SNNs require a large number of time-steps to achieve competitive accuracy which diminishes the energy savings. The number of time-steps can be reduced by training SNNs with spike-based backpropagation from scratch, but that is computationally expensive and slow. To address these challenges, we present a computationally-efficient training technique for deep SNNs. We propose a hybrid training methodology: 1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time-steps for input processing. STDB is performed with a novel surrogate gradient function defined using neuron*s spike time. The weight update is proportional to the difference in spike timing between the current time-step and the most recent time-step the neuron generated an output spike. The SNNs trained with our hybrid conversion-and-STDB training perform at 10X-25X fewer number of time-steps and achieve similar accuracy compared to purely converted SNNs. The proposed training methodology converges in less than 20 epochs of spike-based backpropagation for most standard image classification datasets, thereby greatly reducing the training complexity compared to training SNNs from scratch. We perform experiments on CIFAR-10, CIFAR-100 and ImageNet datasets for both VGG and ResNet architectures. We achieve top-1 accuracy of 65.19% for ImageNet dataset on SNN with 250 time-steps, which is 10X faster compared to converted SNNs with similar accuracy.
This paper proposes methods to initialize and train spiking NNs (SNNs) as an alternative to ANNs, not driven primarily by improved loss or generalization, but by energy efficiency improvements derived from timing-event based sparse operation rather than asynchronous sweeps. The backpropagation method introduced is sensible, as are the experiments on known datasets to show its effectiveness. The paper is well written (apart from the miniscule Figure 3 containing the main result). I recommend acceptance, with caveats: the energy performance is actually not directly calculated, but speculatively estimated, it depends on the computational architecture chosen to implement the respective networks. I point out that ANNs need to be trained first to properly initialize an SNN, so the relative training effort claimed is less impressive, but energy performance does count in actual operational practice - training is (or should) be a small fraction of that.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes) which can potentially lead to higher energy-efficiency in neuromorphic hardware implementations. Many works have shown that an SNN for inference can be formed by copying the weights from a trained Artificial Neural Network (ANN) and setting the firing threshold for each layer as the maximum input received in that layer. These type of converted SNNs require a large number of time-steps to achieve competitive accuracy which diminishes the energy savings. The number of time-steps can be reduced by training SNNs with spike-based backpropagation from scratch, but that is computationally expensive and slow. To address these challenges, we present a computationally-efficient training technique for deep SNNs. We propose a hybrid training methodology: 1) take a converted SNN and use its weights and thresholds as an initialization step for spike-based backpropagation, and 2) perform incremental spike-timing dependent backpropagation (STDB) on this carefully initialized network to obtain an SNN that converges within few epochs and requires fewer time-steps for input processing. STDB is performed with a novel surrogate gradient function defined using neuron*s spike time. The weight update is proportional to the difference in spike timing between the current time-step and the most recent time-step the neuron generated an output spike. The SNNs trained with our hybrid conversion-and-STDB training perform at 10X-25X fewer number of time-steps and achieve similar accuracy compared to purely converted SNNs. The proposed training methodology converges in less than 20 epochs of spike-based backpropagation for most standard image classification datasets, thereby greatly reducing the training complexity compared to training SNNs from scratch. We perform experiments on CIFAR-10, CIFAR-100 and ImageNet datasets for both VGG and ResNet architectures. We achieve top-1 accuracy of 65.19% for ImageNet dataset on SNN with 250 time-steps, which is 10X faster compared to converted SNNs with similar accuracy.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes methods to initialize and train spiking NNs (SNNs) as an alternative to ANNs, not driven primarily by improved loss or generalization, but by energy efficiency improvements derived from timing-event based sparse operation rather than asynchronous sweeps. The backpropagation method introduced is sensible, as are the experiments on known datasets to show its effectiveness. The paper is well written (apart from the miniscule Figure 3 containing the main result). I recommend acceptance, with caveats: the energy performance is actually not directly calculated, but speculatively estimated, it depends on the computational architecture chosen to implement the respective networks. I point out that ANNs need to be trained first to properly initialize an SNN, so the relative training effort claimed is less impressive, but energy performance does count in actual operational practice - training is (or should) be a small fraction of that.
assistant
This paper proposes methods to initialize and train spiking NNs (SNNs) as an alternative to ANNs, not driven primarily by improved loss or generalization, but by energy efficiency improvements derived from timing-event based sparse operation rather than asynchronous sweeps. The backpropagation method introduced is sensible, as are the experiments on known datasets to show its effectiveness. The paper is well written (apart from the miniscule Figure 3 containing the main result). I recommend acceptance, with caveats: the energy performance is actually not directly calculated, but speculatively estimated, it depends on the computational architecture chosen to implement the respective networks. I point out that ANNs need to be trained first to properly initialize an SNN, so the relative training effort claimed is less impressive, but energy performance does count in actual operational practice - training is (or should) be a small fraction of that.