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Exploring Trade-Offs in Spiking Neural Networks.

Florian Bacho¹, Dominique Chu²

¹CEMS, School of Computing, University of Kent, Canterbury CT2 7NF, U.K. fb320@kent.ac.uk.

Neural Computation

|July 31, 2023

Summary

This summary is machine-generated.

Spiking neural networks (SNNs) offer low-power computing but face trade-offs with constraints like time-to-first-spike (TTFS). Relaxing this constraint improves performance, speed, and robustness, benefiting neuromorphic computing development.

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Area of Science:

Neuromorphic Computing
Artificial Intelligence
Computational Neuroscience

Background:

Spiking neural networks (SNNs) are promising for low-power computing, offering an alternative to traditional deep neural networks.
The effectiveness of SNNs depends on performance, energy consumption, speed, and robustness.
Current methods like Fast & Deep use time-to-first-spike (TTFS) constraints for efficiency, but this limits SNN capabilities.

Purpose of the Study:

To investigate the trade-offs between performance, energy consumption, speed, and stability in SNNs under TTFS constraints.
To propose and evaluate a relaxed version of the Fast & Deep method that allows multiple spikes per neuron.
To demonstrate the advantages of unconstrained SNNs over TTFS SNNs for effective learning strategies.

Main Methods:

Exploration of performance, energy consumption, speed, and stability trade-offs in TTFS SNNs.
Proposal of a relaxed Fast & Deep model permitting multiple spikes per neuron.
Experimental comparison of TTFS SNNs with the proposed relaxed model on key performance metrics.

Main Results:

TTFS constraints create trade-offs, sacrificing sparsity and increasing latency for performance and robustness.
Relaxing the spike constraint in Fast & Deep led to higher performance and faster convergence.
The relaxed model demonstrated similar sparsity, comparable latency, and improved robustness to noise compared to TTFS SNNs.

Conclusions:

The TTFS constraint in SNNs presents significant limitations that can be overcome by relaxing the spike limitation.
Unconstrained SNNs, particularly the proposed relaxed Fast & Deep model, offer superior performance, efficiency, and robustness.
This research provides crucial insights for developing advanced learning strategies in neuromorphic computing.