An Efficient Multilayer Spiking Convolutional Neural Network Processor for Object Recognition With Low Bitwidth and Channel-Level Parallelism

Lichen Feng, Yueqi Zhang, Zhangming Zhu

Abstract: Previous studies have shown that the event-driven multilayer spiking convolutional neural network (SCNN) can reduce computational complexity largely while keeping accurate. To fully utilize the advantages of SCNN, this brief proposed an efficient multilayer SCNN processor for object recognition. The interconnection between spiking layers is implemented for the first time. The rank-order coding with mutual and lateral inhibitions enables sparse event transmission. By further combining the spike-centric membrane potential update, channel-level parallel operation, and the low bitwidths of synapse weights and potentials, the proposed design achieves 500 classifications/s, and 68 uJ/classification for recognizing images with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$160\mathbf {\times }250$ </tex-math></inline-formula> resolution, which is superior to the recent works.