EPOC:一个28纳米的5.3 pJ/SOP事件驱动并行神经形态硬件与基于神经调节的在线学习
IEEE transactions on biomedical circuits and systems
|October 2, 2024
概括
我们开发了EPOC,这是一款用于高效,生物灵感的人工智能的新型神经形态处理器. 这种由事件驱动的系统提高了神经形态硬件的学习速度和准确性,为类似人类的智能铺平了道路.
科学领域:
- 神经形态工程的神经形态工程
- 人工智能的人工智能
- 生物灵感计算 生物灵感计算
背景情况:
- 神经形态硬件承诺节能,适应性强的AI,但缺乏统一的学习框架.
- 现有的系统未充分利用基于尖峰的并行性,限制了计算效率和规模.
研究的目的:
- 开发一个统一的,以事件驱动的,大规模并行的多核神经形态在线学习处理器 (EPOC).
- 为各种SNN学习算法引入基于神经调节的框架.
- 为了提高神经形态系统中的计算效率和学习准确性.
主要方法:
- 开发了EPOC,这是一款多核神经形态处理器,采用了新的事件驱动计算方法.
- 实施了基于神经调节的在线学习框架,支持监督SNN学习.
- 使用低内存流式单样学习策略.
- 利用并行多通道和多核心架构进行增强处理.
主要成果:
- 在MNIST (99.2%),NMNIST (98.2%) 和DVS手势 (94.3%) 上,EPOC实现了最先进的精度.
- 与基线架构相比,证明了9.9倍的时间效率改进.
- 在 EPOC 上进行本地学习,时间效率比全球学习提高了 2.9 倍.
- 实现了高能效 (5.3 pJ/SOP) 和吞吐量 (328 GOPS/51 GSOPS).
结论:
- EPOC为神经形态硬件提供了一个统一,高效和生物可信的学习框架.
- 事件驱动的设计和并行架构显著提高了计算效率.
- EPOC在节能硬件中推进了类似人类智能的发展.
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