Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a novel method of training spiking neural networks (SNNs) by matching the intermediate feature representations of SNNs with pretrained ANNs. The method is on-chip and local, allowing SNNs to be learned directly on neuromorphic hardware. Experiments on image classification benchmarks demonstrate that the proposed LTL rule performs comparably to other ANN-to-SNN conversion methods. Moreover, LTL is able to train SNNs with high accuracy even under small time windows, which is important to limit the number of synaptic operations of SNNs. LTL trained SNNs can also be trained with a small number of training iterations relative to STBP. Theoretically, STBP has lower or comparable memory and time complexity to baselines. Finally, LTL is robust to a variety of noises.
strengths_and_weaknesses: **Originality**
The proposed method appears novel. To my knowledge, this method is the first on-chip ANN-to-SNN conversion method that uses layerwise feature-based transfer of information from an ANN to an SNN. 

**Quality**
The method is not theoretically motivated. However, this is not strictly necessary if there are good empirical results.

Experiments are extensive. The authors demonstrate that LTL achieves comparable performance to many baseline methods (see Table 1). However, given that one of the main advantages of LTL is its efficiency and robustness, the experiments in section 3.2 - 3.5 could be significantly expanded to highlight this. In particular, the authors may want to consider additional SNN training baselines on top of STBP. Moreover, in section 3.4 the authors study the asymptotic time and memory complexity of LTL. It would be valuable to empirically validate these asymptotic trends. Finally, the experiments in section 3.5 are interesting, but unfortunately difficult to contextualize without a baseline method to compare with.

If the authors can adequately address the concerns on the experiments, then I think the overall quality of the paper can be dramatically increased.

**Clarity**
The paper is generally well written. The mathematical notation is clear and figures are well illustrated.

**Significance**
The paper is potentially quite significant. It appears to be a more efficient and robust method of training SNNs on-chip than previous method, while maintaining high accuracy. However, further empirical experiments are needed to better justify these claims. Given that the experiments are improved, the paper would be an important contribution to the field of SNN training, and may potentially be used on actual neuromorphic chips.
questions: How does the number of synaptic operations of LTL compare with other SNN-to-ANN conversion methods?

Does the asymptotic analysis presented in section 3.4 match empirical observations?

How does the noise robustness of LTL compare with other SNN-to-ANN conversion methods?
limitations: The authors touch on some limitations of the work in the conclusion: they do not study architectures for sequential data such as RNNs and transformers. It would also be good to note any practical difficulties that might arise when implementing LTL on neuromorphic hardware.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a Local Tandem Learning (LTL) method to train spiking neural networks by distilling knowledge from a pre-trained ANN in a layer-wise way. Two versions of LTL, offline learning and online learning, are introduced and the on-chip implementation is discussed. Experiments on CIFAR-10, CIFAR-100, and Tiny ImageNet show competitive performance and robustness to hardware-related noises.
strengths_and_weaknesses: Strengths:
1. The proposed LTL method considers both offline and online learning, and the on-chip implementation is discussed. It is novel to transfer knowledge of ANNs by distilling intermediate feature representations instead of reusing network weights.
2. Experiments from many perspectives are conducted, including classification accuracy, synaptic operations, convergence of learning, and robustness to hardware-related noises.

Weakness:
1. This paper emphasizes the on-chip implementation throughout the paper, but no real on-chip implementation is demonstrated, and more importantly, it is not explained clearly how conceptually the learning from a pre-trained ANN (rather than direct SNN training) can be implemented on chips. Figure 1(a) shows that the inference of both SNN and ANN is required in order to calculate the loss for each layer. But how can the inference of ANNs be implemented on neuromorphic chips without conversion to SNNs? Or is the inference of ANNs realized on common hardware and the signal is passed to chips? If so, is it only partly on-chip learning, and what is the advantage over off-chip learning since it still requires much off-chip computation (the inference of ANNs)? If the motivation is to address the real device mismatch problem, it is better to show some preliminary real on-chip results, otherwise the simulation of noises can also be leveraged by off-chip methods for finetuning. Or some comparison with finetuning by other off-chip methods in this simulation scenario is ok. There is some related work to address the real device mismatch problem [1]. They combine the on-chip and off-chip computation to correct real device mismatch by surrogate gradient learning. What is the comparison of the proposed method to their method? There should be some discussion and clarification to the above questions.

2. The state-of-the-art ANN-to-SNN conversion method outperforms the results (accuracy and latency) in this paper [3]. Since LTL also requires knowledge from pre-trained ANNs and involves more complex training procedure than conversion methods (conversion methods only need to train ANNs, LTL should first train ANNs and then train SNNs with knowledge from the pretrained ANNs), what is the advantage of LTL if ANN-to-SNN can achieve better results?

3. Experiments do not include the large-scale ImageNet dataset. Typical ANN-to-SNN conversion methods include results on large-scale datasets [2,3] and recently even direct training methods [4,5,6,7] have demonstrated competitive performance on ImageNet. Since this paper also transfer knowledge from ANNs, it would be better to include ImageNet results to show the scalability of the method.

4. Is the proposed method applicable to dynamic DVS datasets, such as CIFAR10-DVS? SNNs can be more powerful to process DVS inputs and have demonstrated strong results [4,5,6,7]. Since the proposed method relies on ANNs, is it limited to static inputs? How is the flexibility compared with direct training methods?

[1] Cramer B, Billaudelle S, Kanya S, et al. Surrogate gradients for analog neuromorphic computing. Proceedings of the National Academy of Sciences, 2022, 119(4): e2109194119.

[2] Li Y, Deng S, Dong X, et al. A free lunch from ANN: Towards efficient, accurate spiking neural networks calibration. International Conference on Machine Learning, 2021.

[3] Bu T, Fang W, Ding J, et al. Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks. International Conference on Learning Representations, 2022.

[4] Zheng H, Wu Y, Deng L, et al. Going deeper with directly-trained larger spiking neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, 2021.

[5] Li Y, Guo Y, Zhang S, et al. Differentiable spike: Rethinking gradient-descent for training spiking neural networks. Advances in Neural Information Processing Systems, 2021.

[6] Meng Q, Xiao M, Yan S, et al. Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.

[7] Deng S, Li Y, Zhang S, et al. Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting. International Conference on Learning Representations, 2022.

questions: See Weakness above.
limitations: There is no potential negative societal impact.
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: Summary:
This paper proposes a spiking neural network (SNN) learning rule, namely Local Tandem Learning (LTL). It combines the teacher-student learning framework and a novel layer-wise local learning method for ANN-to-SNN conversions. Experiments demonstrate near lossless knowledge transfer can be achieved on Cifar-10, Cifar-100 and Tiny-ImageNet datasets with fast convergence and low computational complexity. Notably, authors also show that the LTL rule is hardware friendly. It can address the hardware-related noises through on-chip learning, which remains a grand challenge for deploying SNNs on the ultra-low-power mixed-signal neuromorphic computing chips.

Overall, this paper is well-written and easy to understand. In terms of technical quality, the proposed local tandem learning rule is novel, significant, and well-motivated. It provides an appreciable advance in the field of neuromorphic computing. 




strengths_and_weaknesses: Pros:
The topic of this paper is of sufficient interests to the neuromorphic computing community. As clearly summarized in the paper, the LTL learning rule has several attractive feats:
1.	The proposed Teacher-Student learning framework is a generalized knowledge transfer approach. It can be applied for different neuron models, which is out of the reach of the existing ANN-to-SNN conversion methods.
2.	The proposed local learning approach is a valuable exploration beyond the mainstream e2e SNN training algorithms. By decoupling the learning process for each layer, the proposed method reduces the required spatial-temporal credit assignment to only the temporal domain, which greatly improve the network convergence speed and can also alleviate the compounding gradient approximation errors of the surrogate gradient method.
3.	This paper also addresses an under-explored direction of algorithm-hardware co-development. This paper shows the proposed LTL rule can be easily implemented on-chip using only locally available information.  

Cons:
1.	For experimental results in Table 1, the comparisons are mainly focus on the ANN-to-SNN conversion methods, while authors only compared with one direct training method. I suggest authors to include other more recent direct training algorithms to show the superiority. 
2.	For online LTL learning, as the first few time-steps are generally unstable, with inaccurate firing rate estimation, hence result in noisy training. I am wondering how this affects the learning performance.
3.	In the hardware-related noise experiments, authors using the Gaussian noise to simulate the parameter mismatch and thermal noise. However, in the real hardware implementation, these noises may not follow the Gaussian distribution.  I am curious why authors use the Gaussian Noise in this experiment.

questions: 1. Please summarize the key differences from previous ANN-to-SNN work[1]
2. Please fix the typo of one of the works in Table 1: OCFS  

Ref. 
Wu J, Chua Y, Zhang M, et al. A tandem learning rule for effective training and rapid inference of deep spiking neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021.
limitations: N/A
review_title: null

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes a generalized learning rule called Local Tandem Learning (LTL) which the SNN mimics the feature representation of a pre-trained ANN through local loss functions. The intermediate feature representation of a pre-trained ANN is transferred to SNN through layer-wise local loss functions. This method has not only high accuracy but also fast convergence It has two versions: offline and online and both versions have high performance. In the experiments, the authors verify the high performance of this method on CIFAR-10, CIFAR-100, and ImageNet dataset. Besides, this method is hardware friendly and can be easily implemented on-chip which is robust to hardware-related noises.
strengths_and_weaknesses: Strengths:
(1)	The method proposes a local loss function trains the SNN by learning the intermediate feature representation of the middle layers of the pre-trained ANN. It combines the advantages of ANN-to-SNN conversion and gradient-based training methods. This work is very valuable.
(2)	The authors have done sufficient experiments. This method reduces the epochs required for training to convergence. It can achieve rapid network convergence within five training epochs on the CIFAR-10 dataset.
(3)	The accuracy of SNN trained by this method gap to the teacher ANN is almost zero. Compared with other SOTA learning algorithms, the proposed methods also can achieve comparable performance.
Weaknesses:
(1)	Background knowledge of the Teacher-Student (T-S) learning approach is not introduced and some introductions about this aspect can be added.

questions: (1)	The comparison of accuracy between the Local Tandem Learning (LTL) method and only use the surrogate gradient method to training SNN can be added to verify that this method learns the knowledge in ANN.
(2)	More architectures such as ResNet can be used to verify the high performance of this algorithm rather than just VGG network architecture in order to better demonstrate the advantages of this approach in different network structures.

limitations: The authors put forward a generalized learning rule, termed Local Tandem Learning (LTL) which the SNN mimics the feature representation of a pre-trained ANN through local loss functions. It uses the intermediate feature represents of the pre-trained ANN to supervise the training of SNNs. This method considers the problem that the SNN is difficult to train because of the discrete spikes. It improves the accuracy and computational complexity of SNN. The research of this article is very useful and valuable.
review_title: null

Meta-review: metareview: This paper proposes a novel method of training spiking neural networks (SNNs) by matching the intermediate feature representations of SNNs with pre-trained ANNs. The method is on-chip and local, allowing SNNs to be learned directly on neuromorphic hardware. 

All reviewers agreed that the problem that the paper target to solve is important, and the proposed method is novel. During the discussion period, the authors successfully addressed the concerns of the reviewers. Therefore, I recommend acceptance.
confidence: Certain
Decision: Accept