Paper: Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks (ICLR-2024)

Reviewer comment (Presentation):
The Figure 2 in the experimental section is relatively blurry.

Author response:
The blurriness in Figure 2 is indeed a result of the overlap of lines, which is expected because SAF-E and OTTT$_ {\\rm O}$ are theoretically equivalent. Conversely, in Figure 4, the non-overlapping lines representing SAF-F and OTTT$_ {\\rm A}$ indicate that they are not equivalent. This result also aligns with our theoretical predictions.

Reviewer comment (Experiments):
The memory compression in Table 1 is also somewhat limited. Are there better results available to demonstrate the effectiveness of the method?

Author response:
Compared to OTTT, SAF achieved a notable reduction of 30\% in memory usage. Similarly, the training time was also reduced by the same percentage, at least. These results are not only significant but also demonstrating substantial differences between the two methods.

Reviewer comment (Experiments):
Are there plans for more experiments to validate SAF's performance in different scenarios?

Author response:
In Section 5, our main objective is to ensure that there are no inconsistencies between our theory and experiment. Our theory suggests that SAF-E is equivalent to OTTT$_ {\\rm O}$, and SAF-F is equivalent to spike representation. These equivalences can be confirmed through the current experiments. Additionally, it is worth noting that OTTT and Spike Representation, as mentioned in Xiao et al. [2022], can be applied to more complex datasets. Given that our methods are in full theoretical agreement with these approaches, this fact clearly supports the scalability of our method. Therefore, it is evident that scalability can be demonstrated without the large and complex experiments. We are willing to add it to our paper if necessary.

Reviewer comment (Experiments):
The authors didn’t compare SAF to BPTT and other successful training methods, requiring a comprehensive understanding.

Author response:
**---The authors didn’t compare SAF to BPTT and other successful training methods, requiring a comprehensive understanding.**  
The primary objective of this paper is indeed to establish the theoretical consistency between SAF-E and OTTT$_{\rm O}$, as well as SAF-F and spike representation. Therefore, comparisons with other methods are beyond the scope of the paper's claims. It is worth noting that previous studies have already demonstrated the superior performance of OTTT over BPTT and other methods.

Reviewer comment (Experiments):
They conducted experiments on a single set of hypermeters, which lacks further exploration of the robustness of the methods. Understanding the effects of various hyperparameters on SAF's performance and how to tune them for various applications would be improved by a more thorough analysis.

Author response:
**---They conducted experiments on a single set of hypermeters, which lacks further exploration of the robustness of the methods. Understanding the effects of various hyperparameters on SAF's performance and how to tune them for various applications would be improved by a more thorough analysis.  
---Regarding the robustness of the training algorithm, can you provide some results that have different settings of $\mathbf{\lambda}$ and $\mathbf{T}$.**  
As mentioned earlier, the paper indeed provides a theoretical guarantee of consistency between SAF-E and OTTT$_{\rm O}$, as well as SAF-F and spike representations. Additionally, it is worth noting that the hyperparameters of SAF are exactly the same as the hyperparameters of OTTT. This implies that conducting a hyperparameter search will yield identical accuracies to OTTT.  However, it is important to note that time and memory usage are not explicitly addressed in the theoretical analysis. To provide a more comprehensive understanding of the practical implications, Figures 3 (A, B) and 5 (A, B) present the training time and memory usage when varying $T$, as it has the most significant impact on these indicators. This allows for a more complete evaluation of the performance of the methods beyond accuracy alone.

Reviewer comment (Evaluation):
The authors have not provided a detailed analysis of the computational complexity of SAF. The authors have empirically demonstrated that SAF reduces memory needs and training time; however, they haven't done a thorough investigation of the computational complexity of the approach. The experiment is currently based on GPU architecture. It would be easier to comprehend the computational resources needed to execute SAF and its potential for real-world applications with a more thorough analysis.

Author response:
**---The authors have not provided a detailed analysis of the computational complexity of SAF. The authors have empirically demonstrated that SAF reduces memory needs and training time; however, they haven't done a thorough investigation of the computational complexity of the approach. The experiment is currently based on GPU architecture. It would be easier to comprehend the computational resources needed to execute SAF and its potential for real-world applications with a more thorough analysis.  
---Can you provide detailed explanations of how much computational overhead is needed when training? What kind of computation is included (float-point computation or spike computation)? I am interested in this because the training, unlike other online training, is based on float-point representation.**

We compare between the computational complexity (FLOPs) of OTTT and SAF during the table below. $c$, $k$, $w$, and $h$ represent the channel size, kernel size, and the width and height of the feature map. Also, $d_{\rm in}$ and $d_{\rm out}$ represent input and output sizes when fully connected. By using SAF, we can halve the computation of weights.Moreover, SAF only requires the retention of spike accumulation (float), while OTTT needs to retain membrane potentials (float), spike trains (spike), and spike accumulation (float). In other words, SAF only uses floating-point computation during training and spike computation during inference.   
We will add this table to Appendix if necessary.
|                        | **OTTT$_ {\\rm O}$/OTTT$_ {\\rm A}$** | **SAF-F/SAF-E (ours)** |
|------------------------|----------------------------------|------------------------|
| FLOPs (Conv2D)         | $2 c^2 k^2 wh$                   | $c^2 k^2 wh$           |
| FLOPs (FC)             | $2 d_ {\\rm in} d_ {\\rm out}$       | $d_{\\rm in} d_ {\\rm out}$|
|------------------------|----------------------------------|------------------------|
| spike accumulation $\\widehat{\\boldsymbol {a}} ^ l[t]$ | $\\checkmark$ | $\\checkmark $|
| spike $\\boldsymbol {s} ^ l[t]$    | $\\checkmark$ | -                      |
| Membrane potential $\widehat{\\boldsymbol {U}}^{l+1}[t]$ |$ \\checkmark$ | -   |

Reviewer comment (Theory):
The theoretical results only show that the proposed method has the same capabilities as OTTT and SpikeRepresentation, and no new guarantee of training is presented there. Hence, the contribution is reduced to find an efficient forward method that fits the training models of OTTT and SpikeRepresentation.

Author response:
**---The theoretical results only show that the proposed method has the same capabilities as OTTT and SpikeRepresentation, and no new guarantee of training is presented there. Hence, the contribution is reduced to find an efficient forward method that fits the training models of OTTT and SpikeRepresentation.**  
SAF offers memory reduction by eliminating the need to calculate membrane potentials during training, as well as halving the time required for weight calculation during forward propagation. In our experiments, SAF achieved a significant reduction of 30\% in memory usage compared to OTTT. Similarly, the training time was also reduced by the same percentage, at least. These results demonstrate substantial differences between the two methods and highlight the significance of SAF's contributions. Moreover, we have established the theoretical consistency of SAF with OTTT and spike representation for both of forward and backward. Based on these contributions, we believe that our paper is suitable for acceptance by ICLR.

Reviewer comment (Reproducibility):
Can you provide detailed explanations of how much computational overhead is needed when training? What kind of computation is included (float-point computation or spike computation)? I am interested in this because the training, unlike other online training, is based on float-point representation.

Author response:
**---The authors have not provided a detailed analysis of the computational complexity of SAF. The authors have empirically demonstrated that SAF reduces memory needs and training time; however, they haven't done a thorough investigation of the computational complexity of the approach. The experiment is currently based on GPU architecture. It would be easier to comprehend the computational resources needed to execute SAF and its potential for real-world applications with a more thorough analysis.  
---Can you provide detailed explanations of how much computational overhead is needed when training? What kind of computation is included (float-point computation or spike computation)? I am interested in this because the training, unlike other online training, is based on float-point representation.**

We compare between the computational complexity (FLOPs) of OTTT and SAF during the table below. $c$, $k$, $w$, and $h$ represent the channel size, kernel size, and the width and height of the feature map. Also, $d_{\rm in}$ and $d_{\rm out}$ represent input and output sizes when fully connected. By using SAF, we can halve the computation of weights.Moreover, SAF only requires the retention of spike accumulation (float), while OTTT needs to retain membrane potentials (float), spike trains (spike), and spike accumulation (float). In other words, SAF only uses floating-point computation during training and spike computation during inference.   
We will add this table to Appendix if necessary.
|                        | **OTTT$_ {\\rm O}$/OTTT$_ {\\rm A}$** | **SAF-F/SAF-E (ours)** |
|------------------------|----------------------------------|------------------------|
| FLOPs (Conv2D)         | $2 c^2 k^2 wh$                   | $c^2 k^2 wh$           |
| FLOPs (FC)             | $2 d_ {\\rm in} d_ {\\rm out}$       | $d_{\\rm in} d_ {\\rm out}$|
|------------------------|----------------------------------|------------------------|
| spike accumulation $\\widehat{\\boldsymbol {a}} ^ l[t]$ | $\\checkmark$ | $\\checkmark $|
| spike $\\boldsymbol {s} ^ l[t]$    | $\\checkmark$ | -                      |
| Membrane potential $\widehat{\\boldsymbol {U}}^{l+1}[t]$ |$ \\checkmark$ | -   |

Reviewer comment (Experiments):
Regarding the robustness of the training algorithm, can you provide some results that have different settings of $\lambda$ and $T$.

Author response:
**---They conducted experiments on a single set of hypermeters, which lacks further exploration of the robustness of the methods. Understanding the effects of various hyperparameters on SAF's performance and how to tune them for various applications would be improved by a more thorough analysis.  
---Regarding the robustness of the training algorithm, can you provide some results that have different settings of $\mathbf{\lambda}$ and $\mathbf{T}$.**  
As mentioned earlier, the paper indeed provides a theoretical guarantee of consistency between SAF-E and OTTT$_{\rm O}$, as well as SAF-F and spike representations. Additionally, it is worth noting that the hyperparameters of SAF are exactly the same as the hyperparameters of OTTT. This implies that conducting a hyperparameter search will yield identical accuracies to OTTT.  However, it is important to note that time and memory usage are not explicitly addressed in the theoretical analysis. To provide a more comprehensive understanding of the practical implications, Figures 3 (A, B) and 5 (A, B) present the training time and memory usage when varying $T$, as it has the most significant impact on these indicators. This allows for a more complete evaluation of the performance of the methods beyond accuracy alone.

Reviewer comment (Experiments):
Also, the current dataset setting is lacking. Only CIFAR-10 experiments are conducted. What about CIFAR-100 and ImageNet?

Author response:
**---Also, the current dataset setting is lacking. Only CIFAR-10 experiments are conducted. What about CIFAR-100 and ImageNet?**  
In Section 5, our main objective is to ensure that there are no inconsistencies between our theory and experiment. Our theory suggests that SAF-E is equivalent to OTTT$_{\rm O}$, and SAF-F is equivalent to spike representation. These equivalences can be confirmed through the current experiments. Additionally, it is worth noting that OTTT and Spike Representation, as mentioned in Xiao et al. [2022], can be applied to more complex datasets. Given that our methods are in full theoretical agreement with these approaches, this fact clearly supports the scalability of our method. Therefore, it is evident that scalability can be demonstrated without the large and complex experiments. We are willing to add it to our paper if necessary.

Reviewer comment (Writing):
There is numerous equations in this paper, and many parts lack detailed reasoning, making it challenging for readers to follow along. For instance, in the section Spike Representation, the intermediate steps of most equations are omitted. And $\mathbf{x}$ lacks explanation.

Author response:
Due to page limitations, it is only possible to include some reasoning in the main text. The detailed reasoning of Section 4 is provided in Appendix A. Also, $x$ is the value of the input data. In the case of CIFAR-10, it is the value of each pixel in the image. This explanation has been added to the main text.

Reviewer comment (Presentation):
What is the purpose of section 2.4.3. The network structure that included in this paper and OTTT do not have feedback connection.

Author response:
The case with feedback is also mentioned in the paper of OTTT. Therefore, we considered SAF with feedback in Section 4.3, and experimental results were stated in Appendix E.

Reviewer comment (Experiments):
Merely validating the consistency between theoretical reasoning and the experimental results on CIFAR10 is insufficient for the author's purpose. It is evident that the author's approach, despite claiming consistency in the computation of reverse gradients with OTTT, yields different training outcomes (accuracy and fire rate). Moreover, based on the results presented by the author, it is clear that the SNN trained using the SAF method outperforms the OTTT method. Since the OTTT method involves more complex datasets, the author needs to demonstrate the superiority of SAF on those datasets as well.

Author response:
It is indeed consistent with SAF-F for spike representation, but not with OTTT${_ \\rm A}$. Section 5.2 confirms this distinction, highlighting the differences between the two methods. Consequently, the firing rates are expected to differ, which is correct. On the other hand, Section 5.1 describes the results of an experiment that confirms the equivalence between SAF-E and OTTT${_ \\rm O}$. The figure presented in this section demonstrates the same firing rates. Thanks to your review, we have added explanations to the figure captions in Section 5, improving readability.

Reviewer comment (Writing):
The paper is in parts poorly written and very hard to follow. It would benefit from substantial language editing. Also, there are some sentences and sections, e.g. 3rd paragraph of the introduction, whose meaning I do not understand at all.

Author response:
**---The paper is in parts poorly written and very hard to follow. It would benefit from substantial language editing. Also, there are some Sentences and sections, e.g. 3rd paragraph of the introduction, whose meaning I do not understand at all.**  
We apologize if there was any confusion. However, we have had native English speakers proofread and confirm that there are no problems.

Reviewer comment (Reproducibility):
The experimental evaluation is kept rather short. While it does seem to confirm the theoretical findings, there is no clear description of the experiments (e.g. the utilized model architecture and training setup), and no source code, which prohibits reproduction of the results.

Author response:
**---The experimental evaluation is kept rather short. While it does seem to confirm the theoretical findings, there is no clear description of the experiments (e.g. the utilized model architecture and training setup), and no source code, which prohibits reproduction of the results.  
---What model architecture and training setup was used for the experiments. As it, they are not reproducible by others.**  
The details of the model used and the training setup can be found in Appendix C, and the source code is available in the Supplementary material. It is important to note that all setups were consistent with the experiments conducted by Xiao et al. [2022].

Reviewer comment (Writing):
The prior work and related concepts upon which the contribution of the authors build is not explained very well. Half of the assumptions and derivations are to be found in other papers, making it almost impossible to fully grasp the paper as a stand alone. It appears to be follow-up work by the authors of previous work, hence the authors don’t seem to find it necessary to fully explain the background concepts.

Author response:
**---The prior work and related concepts upon which the contribution of the authors build is not explained very well. Half of the assumptions and derivations are to be found in other papers, making it almost impossible to fully grasp the paper as a stand alone. It appears to be follow-up work by the authors of previous work, hence the authors don’t seem to find it necessary to fully explain the background concepts**  
The concept of this study is fully explained in Section 4.1. On the other hand, due to page limitations, it is only possible to include some reasoning in the main text. The detailed is provided in Appendix A.   
SAF offers memory reduction by eliminating the need to calculate membrane potentials during training, as well as halving the time required for weight calculation during forward propagation. In our experiments, SAF achieved a significant reduction of 30\% in memory usage compared to OTTT. Similarly, the training time was also reduced by the same percentage, at least. These results demonstrate substantial differences between the two methods and highlight the significance of SAF's contributions. Moreover, we have established the theoretical consistency of SAF with OTTT and spike representation for both of forward and backward. Based on these contributions, our paper goes beyond being a mere follow-up, and we believe that it is suitable for acceptance by ICLR.

Reviewer comment (Theory):
It is not clear to me why the approach works for small time steps. As stated in section 3.2 Spike representation assumes a rather large latency (T -> inf) to work. Does that not also apply to SAF? In Fig. 5 you show that firing rates for OTTT and SAF are NOT identical. Then why do your experiments assume T=6? Where does the averaged spike representation come from in practice?

Author response:
**---It is not clear to me why the approach works for small time steps. As stated in section 3.2 Spike representation assumes a rather large latency (T $\to \inf$) to work. Does that not also apply to SAF? In Fig. 5 you show that firing rates for OTTT and SAF are NOT Identical. Then why do your experiments assume T=6? Where does the averaged spike representation come from in practice?**  
There are two reasons why we set $T=6$. First, OTTT's original paper (Xiao et al. [2022]) sets $T=6$. Second, in the case of CIFAR-10, the original paper on spike representation (Meng et al. [2022]) compares the four cases of $T=20,15,10,5$. The results show that the accuracy drop was less than 1\%, and SOTA was achieved at $T=5$. Since SAF-F and OTTT$ _ \rm{A}$ are different at $T=6$, it is unnecessary to increase $T$ to obtain similar results.    
It is indeed consistent with SAF-F for spike representation, but not with OTTT$_ \rm{A}$ . Section 5.2 confirms this distinction, highlighting the differences between the two methods. Consequently, the firing rates are expected to differ, which is correct. On the other hand, Section 5.1 describes the results of an experiment that confirms the equivalence between SAF-E and OTTT$ _ \rm{O}$. The figure presented in this section demonstrates the same firing rates. Thanks to your review, we have added explanations to the figure captions in Section 5, improving readability.

Reviewer comment (Writing):
In Section 3, Spike Representation, what is x for the weighted average input? Not mentioned before.

Author response:
**---In Section 3, Spike Representation, what is x for the weighted average input? Not mentioned before**  
$x$ is the value of the input data. In the case of CIFAR-10, it is the value of each pixel in the image. This explanation has been added to the main text.

Reviewer comment (Reproducibility):
What model architecture and training setup was used for the experiments. As it is, they are not reproducible by others.

Author response:
**---The experimental evaluation is kept rather short. While it does seem to confirm the theoretical findings, there is no clear description of the experiments (e.g. the utilized model architecture and training setup), and no source code, which prohibits reproduction of the results.  
---What model architecture and training setup was used for the experiments. As it, they are not reproducible by others.**  
The details of the model used and the training setup can be found in Appendix C, and the source code is available in the Supplementary material. It is important to note that all setups were consistent with the experiments conducted by Xiao et al. [2022].