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Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks

Ilkin Aliyev, Tosiron Adegbija

TL;DR

This work addresses how training hyperparameters influence sparsity and hardware efficiency in Spiking Neural Networks. It compares surrogate gradient functions (arctangent vs fast sigmoid) and tunes the leak factor $\beta$ and membrane threshold $\theta$ using an FPGA-based accelerator and SVHN data. The results show that fast sigmoid reduces firing activity and, with appropriate tuning, can achieve substantial hardware gains (up to $48\%$ lower latency and $2.88\%$ accuracy loss, and about $1.72\times$ higher FPS/W) compared with prior approaches. The findings underscore the importance of hyperparameter fine-tuning for designing efficient neuromorphic accelerators and suggest broader exploration across datasets and hyperparameters like loss functions.

Abstract

The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in SNNs is uniquely shaped by the dataset and training hyperparameters. This work reveals novel insights into the impacts of training on hardware performance. Specifically, we explore the trade-offs between model accuracy and hardware efficiency. We focus on three key hyperparameters: surrogate gradient functions, beta, and membrane threshold. Results on an FPGA-based hardware platform show that the fast sigmoid surrogate function yields a lower firing rate with similar accuracy compared to the arctangent surrogate on the SVHN dataset. Furthermore, by cross-sweeping the beta and membrane threshold hyperparameters, we can achieve a 48% reduction in hardware-based inference latency with only 2.88% trade-off in inference accuracy compared to the default setting. Overall, this study highlights the importance of fine-tuning model hyperparameters as crucial for designing efficient SNN hardware accelerators, evidenced by the fine-tuned model achieving a 1.72x improvement in accelerator efficiency (FPS/W) compared to the most recent work.

Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks

TL;DR

This work addresses how training hyperparameters influence sparsity and hardware efficiency in Spiking Neural Networks. It compares surrogate gradient functions (arctangent vs fast sigmoid) and tunes the leak factor and membrane threshold using an FPGA-based accelerator and SVHN data. The results show that fast sigmoid reduces firing activity and, with appropriate tuning, can achieve substantial hardware gains (up to lower latency and accuracy loss, and about higher FPS/W) compared with prior approaches. The findings underscore the importance of hyperparameter fine-tuning for designing efficient neuromorphic accelerators and suggest broader exploration across datasets and hyperparameters like loss functions.

Abstract

The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in SNNs is uniquely shaped by the dataset and training hyperparameters. This work reveals novel insights into the impacts of training on hardware performance. Specifically, we explore the trade-offs between model accuracy and hardware efficiency. We focus on three key hyperparameters: surrogate gradient functions, beta, and membrane threshold. Results on an FPGA-based hardware platform show that the fast sigmoid surrogate function yields a lower firing rate with similar accuracy compared to the arctangent surrogate on the SVHN dataset. Furthermore, by cross-sweeping the beta and membrane threshold hyperparameters, we can achieve a 48% reduction in hardware-based inference latency with only 2.88% trade-off in inference accuracy compared to the default setting. Overall, this study highlights the importance of fine-tuning model hyperparameters as crucial for designing efficient SNN hardware accelerators, evidenced by the fine-tuned model achieving a 1.72x improvement in accelerator efficiency (FPS/W) compared to the most recent work.
Paper Structure (8 sections, 4 equations, 2 figures)

This paper contains 8 sections, 4 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Spiking Neuron Model
  4. Surrogate Approximation Functions
  5. Surrogate Gradient Fine-Tuning and Hyperparameter Search Results
  6. Experimental Setup
  7. Results
  8. Conclusion

Figures (2)

  • Figure 1: Cross-comparison results for arctangent and fast sigmoid surrogate functions over varying derivative scaling factors
  • Figure 2: Cross-sweep results for $\beta$ and $\theta$ parameters.