A Fast and Energy-Efficient SNN Processor With Adaptive Clock/Event-Driven Computation Scheme and Online Learning
Sixu Li, Zhaomin Zhang, Ruixin Mao, Jianbiao Xiao, Liang Chang, Jun Zhou
Abstract
In the recent years, the spiking neural network (SNN) has attracted increasing attention due to its low energy consumption and online learning potential. However, the design of SNN processor has not been thoroughly investigated in the past, resulting in limited performance and energy consumption. In this work, a fast and energy-efficient SNN processor with adaptive clock/event-driven computation scheme and online learning capability has been proposed. Several techniques have been proposed to reduce the computation time and energy consumption, including Adaptive Clock- and Event-Driven Computing Scheme, Neighboring PE Borrowing Technique, Compressed Spike Routing Technique and Reconfigurable PE for Inference and Learning. Implemented on a Virtex-7 FPGA, the proposed design achieves computation time of 3.15 ms/image, inference energy consumption of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.028~\mu $ </tex-math></inline-formula> J/synapse/image and online learning energy consumption of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.297~\mu $ </tex-math></inline-formula> J/synapse/image for the MNIST 10-class dataset, which outperform several state-of-the-art SNN processors. The proposed SNN processor is suitable for real-time and energy-constrained applications.