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

Wangdan Liao ¹ Fei Chen ^2,3 Changyue Liu ¹ Weidong Wang ^1,2,3* Hongyun Liu ^2,3*

• ¹ Beihang University, Beijing, China
• ² Medical Innovation Research Division, People's Liberation Army General Hospital, Beijing, Beijing, China
• ³ Key Laboratory of Biomedical Engineering and Translational Medicine, People 's Liberation Army General Hospital, Beijing, Beijing Municipality, China

Spiking Neural Networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks, with potential advantages in power efficiency due to their eventdriven 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 called SpikeAtConv, designed to enhance computational efficacy and task accuracy.The architecture features optimized spiking modules that facilitate the processing of spatiotemporal 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.