FPGA-Based Implementation of an Event-Driven Spiking Multi-Kernel Convolution Architecture

Jian Zhang, Lichen Feng, Tengbo Wang, Wei Shi, Yuechao Wang, Guohe Zhang

Abstract: This brief presents an event-driven spiking multi-kernel convolution architecture for processing address-event representation (AER) streams from dynamic vision sensor (DVS) chips. The processor architecture is designed based on leaky integrate-and-fire (LIF) neural model, and employs pipeline scheme to accelerate data processing. A new scheme for arranging neuron membrane potentials and kernels in memories is proposed, which enables row-by-row kernel processing and accelerates the multi-kernel convolution computation. An FPGA prototype of the proposed architecture is implemented on a Xilinx Zynq FPGA development board with 100 MHz clock frequency. The proposed processor architecture computes 64 filters with configurable kernel size (from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1 \times 1$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32 \times 32$ </tex-math></inline-formula> ) on input flow, obtaining the latency of 0.10 us to 10.33 us for convoluting an event, and the energy of 0.12 nJ and 12.08 nJ per event per convolution, respectively.