一句话：提出硬件事件驱动的 SNN 仿真系统 NeuroEngine，加速支持两阶段神经元与突触延迟的高级脉冲神经网络。
类脑计算旨在理解大脑认知机制并推动计算机科学，脉冲神经网络（SNN）是其重要模型。为利用更高级的机制，需要仿真带两阶段神经元与突触延迟的先进 SNN；但现有 SNN 仿真方法因采用软件实现或基于硬件却时间驱动的执行机制，速度慢且能效低。本文提出 NeuroEngine，一个基于硬件的事件驱动仿真系统，面向高级类脑计算，通过事件驱动方式避免时间驱动带来的开销，以更高效地仿真复杂 SNN。摘要未报告具体实验结果，评估效果需见正文。
Brain-inspired computing aims to understand the cognitive mechanisms of a brain and apply them to advance various areas in computer science. Deep learning is an example to greatly improve the field of pattern recognition and classification by utilizing an artificial neural network (ANN). To exploit advanced mechanisms of a brain and thus make more great advances, researchers need a methodology that can simulate neural networks with higher computational capabilities such as advanced spiking neural networks (SNNs) with two-stage neurons and synaptic delays. However, existing SNN simulation methodologies are too slow and energy-inefficient due to their software-based simulation or hardware-based but time-driven execution mechanisms.
DOI 原文 ·
@inproceedings{paperbot6119,
title = {NeuroEngine: a hardware-based event-driven simulation system for advanced brain-inspired computing},
author = {Hunjun Lee and Chanmyeong Kim and Yujin Chung and Jangwoo Kim},
booktitle = {ASPLOS},
year = {2021},
doi = {10.1145/3445814.3446738}
}