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 $0.028~\mu $ J/synapse/image and online learning energy consumption of $0.297~\mu $ 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.

中文翻译：

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具有自适应时钟/事件驱动计算方案和在线学习的快速节能型SNN处理器

近年来，尖峰神经网络（SNN）由于其低能耗和在线学习潜力而受到越来越多的关注。但是，过去尚未对SNN处理器的设计进行彻底研究，从而导致性能和能耗受到限制。在这项工作中，提出了一种具有自适应时钟/事件驱动计算方案和在线学习能力的快速节能的SNN处理器。已经提出了几种减少计算时间和能耗的技术，包括自适应时钟和事件驱动计算方案，相邻PE借用技术，压缩峰值路由技术以及用于推理和学习的可重构PE。该设计在Virtex-7 FPGA上实现，计算时间为3.15 ms /图像，推理能耗为 $ 0.028〜\亩$ J /突触/图像和在线学习的能量消耗 $ 0.297〜\亩$ MNIST 10类数据集的J /突触/图像，其性能优于几种最先进的SNN处理器。所提出的SNN处理器适用于实时和能量受限的应用。