The Potential of Combined Learning Strategies to Enhance Energy Efficiency of Spiking Neuromorphic Systems

TL;DR

This paper introduces a novel combined learning approach for Convolutional Spiking Neural Networks that enhances energy efficiency and accuracy in neuromorphic systems by integrating PSTDP and power law-dependent STDP.

Contribution

It proposes a new combined learning method for CSNNs that reduces parameters and energy consumption while maintaining accuracy, suitable for hardware implementation.

Findings

Reduced energy consumption in CSNNs.

Maintained accuracy with fewer training parameters.

Validated approach through architecture comparisons.

Abstract

Ensuring energy-efficient design in neuromorphic computing systems necessitates a tailored architecture combined with algorithmic approaches. This manuscript focuses on enhancing brain-inspired perceptual computing machines through a novel combined learning approach for Convolutional Spiking Neural Networks (CSNNs). CSNNs present a promising alternative to traditional power-intensive and complex machine learning methods like backpropagation, offering energy-efficient spiking neuron processing inspired by the human brain. The proposed combined learning method integrates Pair-based Spike Timing-Dependent Plasticity (PSTDP) and power law-dependent Spike-timing-dependent plasticity (STDP) to adjust synaptic efficacies, enabling the utilization of stochastic elements like memristive devices to enhance energy efficiency and improve perceptual computing accuracy. By reducing learning parameters while maintaining accuracy, these systems consume less energy and have reduced area overhead, making them more suitable for hardware implementation. The research delves into neuromorphic design architectures, focusing on CSNNs to provide a general framework for energy-efficient computing hardware. Various CSNN architectures are evaluated to assess how less trainable parameters can maintain acceptable accuracy in perceptual computing systems, positioning them as viable candidates for neuromorphic architecture. Comparisons with previous work validate the achievements and methodology of the proposed architecture.