S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks
Abstract: Spiking Neural Networks (SNNs) are biologically plausible models that have been identified as potentially apt for deploying energy-efficient intelligence at the edge, particularly for sequential learning tasks. However, training of SNNs poses significant challenges due to the necessity for precise temporal and spatial credit assignment. Back-propagation through time (BPTT) algorithm, whilst the most widely used method for addressing these issues, incurs high computational cost due to its temporal dependency. In this work, we propose S-TLLR, a novel three-factor temporal local learning rule inspired by the Spike-Timing Dependent Plasticity (STDP) mechanism, aimed at training deep SNNs on event-based learning tasks. Furthermore, S-TLLR is designed to have low memory and time complexities, which are independent of the number of time steps, rendering it suitable for online learning on low-power edge devices. To demonstrate the scalability of our proposed method, we have conducted extensive evaluations on event-based datasets spanning a wide range of applications, such as image and gesture recognition, audio classification, and optical flow estimation. S-TLLR achieves comparable accuracy to BPTT (within $\pm2\%$ for most tasks), while reducing memory usage by $5-50\times$ and multiply-accumulate (MAC) operations by $1.3-6.6\times$, particularly when updates are restricted to the last few time-steps.
Submission Length: Regular submission (no more than 12 pages of main content)
Code: https://github.com/mapolinario94/S-TLLR
Assigned Action Editor: ~Sebastian_Tschiatschek1
Submission Number: 2489
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