SpikeAtConv: An Integrated Spiking-Convolutional Attention Architecture for Energy-Efficient Neuromorphic Vision Processing

TL;DR

This paper presents SpikeAtConv, a new spiking neural network architecture that improves energy efficiency and performance on visual tasks by integrating convolutional attention mechanisms, bridging the gap with traditional neural networks.

Contribution

Introduction of SpikeAtConv, an integrated spiking-convolutional attention architecture that enhances neuromorphic vision processing efficiency and accuracy.

Findings

Narrowed performance gap with traditional neural networks on image classification.

Demonstrated energy-efficient processing of spatio-temporal visual data.

Validated effectiveness on standard benchmarks.

Abstract

Spiking Neural Networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks, with potential advantages in power efficiency due to their event-driven computation. Despite their promise, SNNs have yet to achieve competitive performance on complex visual tasks, such as image classification. This study introduces a novel SNN architecture designed to enhance computational efficacy and task accuracy. The architecture features optimized pulse modules that facilitate the processing of spatio-temporal patterns in visual data, aiming to reconcile the computational demands of high-level vision tasks with the energy-efficient processing of SNNs. Our evaluations on standard image classification benchmarks indicate that the proposed architecture narrows the performance gap with traditional neural networks, providing insights into the design of more efficient and capable neuromorphic computing systems.