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 presents a fine-tuning method of models converted from standard encoding and SGD training to Spike/NN*s. The key point of the paper is that directly training S/NN*s with spike-back-prop is slow and inefficient, while directly inferencing with converted models is also inefficient due to the large integration window required to get a good estimate of the spiking neuron potential. The authors claim, and to a good extent show that, their proposed method is best of both worlds: train the models efficiently with standard encoding / SGD, this is something we know works and scale well, then convert and fine-tune with spike-backprop to get models that perform well under a shorter integration window, and thus are more efficient at inference time. The intuition is that models can achieve shorter integration windows while keeping good results because, under the assumptions made by the proposed algorithm, the fine-tuning is effectively unrolling neuron dynamics that can be trained with BPPT, in a way similar to LSTM/Recurrent models. In that case, since model dynamics are taken into account during fine-tuning, it results in better performance even under shorter time-windows. This is an interesting concept, since the training doesn*t only consider a mean-field estimate of the spike-activation, but it looks at spiking neuron dynamics with an higher granularity. The paper is well written, clear and easy to understand. Results are comparatively competitive and code is made available.
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 presents a fine-tuning method of models converted from standard encoding and SGD training to Spike/NN*s. The key point of the paper is that directly training S/NN*s with spike-back-prop is slow and inefficient, while directly inferencing with converted models is also inefficient due to the large integration window required to get a good estimate of the spiking neuron potential. The authors claim, and to a good extent show that, their proposed method is best of both worlds: train the models efficiently with standard encoding / SGD, this is something we know works and scale well, then convert and fine-tune with spike-backprop to get models that perform well under a shorter integration window, and thus are more efficient at inference time. The intuition is that models can achieve shorter integration windows while keeping good results because, under the assumptions made by the proposed algorithm, the fine-tuning is effectively unrolling neuron dynamics that can be trained with BPPT, in a way similar to LSTM/Recurrent models. In that case, since model dynamics are taken into account during fine-tuning, it results in better performance even under shorter time-windows. This is an interesting concept, since the training doesn*t only consider a mean-field estimate of the spike-activation, but it looks at spiking neuron dynamics with an higher granularity. The paper is well written, clear and easy to understand. Results are comparatively competitive and code is made available.
assistant
This paper presents a fine-tuning method of models converted from standard encoding and SGD training to Spike/NN*s. The key point of the paper is that directly training S/NN*s with spike-back-prop is slow and inefficient, while directly inferencing with converted models is also inefficient due to the large integration window required to get a good estimate of the spiking neuron potential. The authors claim, and to a good extent show that, their proposed method is best of both worlds: train the models efficiently with standard encoding / SGD, this is something we know works and scale well, then convert and fine-tune with spike-backprop to get models that perform well under a shorter integration window, and thus are more efficient at inference time. The intuition is that models can achieve shorter integration windows while keeping good results because, under the assumptions made by the proposed algorithm, the fine-tuning is effectively unrolling neuron dynamics that can be trained with BPPT, in a way similar to LSTM/Recurrent models. In that case, since model dynamics are taken into account during fine-tuning, it results in better performance even under shorter time-windows. This is an interesting concept, since the training doesn*t only consider a mean-field estimate of the spike-activation, but it looks at spiking neuron dynamics with an higher granularity. The paper is well written, clear and easy to understand. Results are comparatively competitive and code is made available.