基于峰值的时间域模拟加权总和计算模型,用于实现多层神经网络的极低功耗VLSI实现
Quan Wang1, Hakaru Tamukoh1,2, Takashi Morie1,2
1Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan.
Frontiers in neuroscience
|September 29, 2025
概括
本研究介绍了一种用于深度神经网络 (DNN) 的新型时域模拟计算模型. 新模型显著提高了人工智能硬件至关重要的乘积运算中的能源效率.
科学领域:
- 电子工程 电子工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 深度神经网络 (DNN) 在很大程度上依赖于乘积 (MAC) 操作,导致数字处理器的高功耗.
- 在互补金属氧化物半导体 (CMOS) 非常大规模集成 (VLSI) 电路中的模拟计算为这些任务提供了超低功耗的途径.
研究的目的:
- 为DNN提出一个新的时间域模拟加权总和计算模型.
- 为此拟议的模型开发和实施VLSI电路.
- 为了证明DNN的节能内存计算 (IMC).
主要方法:
- 一个时间域模拟加权总和计算模型是基于一个整合和发射尖端神经元模型开发的.
- VLSI电路的设计是利用电容器中的短暂充电/放电过程进行模拟计算.
- 积极和负权重被单独处理,为后续层产生不同的时间,没有减去.
主要成果:
- 一个概念验证的CMOS电路验证了加权和运算.
- 取得的精度超过了4位.
- 证明了237.7Tera每秒每瓦特 (TOPS/W) 的卓越能效,超过了最先进的数字AI处理器.
结论:
- 拟议的时间域模拟模型为DNN计算提供了一种高能效的方法.
- 这种方法适用于中等精度的密集内存计算 (IMC).
- 它显著减少了与模拟数字转换器 (ADC) 相关的开销.
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