对于人工智能应用的FinFET 6T-SRAM全数字内存计算:概述和分析
Waqas Gul1, Maitham Shams1, Dhamin Al-Khalili1
1Department of Electronics, Carleton University, 1125 Colonel Bay Drive, Ottawa, ON K1S 5B6, Canada.
Micromachines
|August 26, 2023
概括
这项研究分析了数字信号域内存计算 (CIM) 使用6T-SRAM细胞用于AI硬件. 它比较了近内存计算和内存计算,突出了FinFET 6T-SRAM性能,以实现高效的AI加速.
科学领域:
- 计算机工程 计算机工程
- 人工智能 硬件 硬件
- 半导体设备 半导体设备
背景情况:
- 现代人工智能硬件,特别是神经网络 (NN),由于频繁的离芯片数据访问,面临性能下降.
- 计算内存 (CIM) 架构对于减少这些数据访问瓶至关重要.
- 现有的混合信号CIM方法在比特精度和信号边缘方面存在局限性,而全数字CIM则以硬件开销为代价提供了改进.
研究的目的:
- 为了分析验证,数字信号域6T-SRAM CIM解决方案用于AI硬件.
- 将近内存计算 (NMC) 和内存计算 (IMC) 实现进行分类和比较.
- 在AI计算系统中研究FinFET 6T-SRAM细胞的性能和局限性.
主要方法:
- 数字信号领域CIM解决方案的分类为NMC和基于SRAM的自定义IMC.
- 专注于乘法和积累 (MAC) 操作,这种操作在卷积神经网络 (CNN) 中常见.
- 分析FinFET 6T-SRAM细胞特征,包括尺寸,电压和工艺变化.
主要成果:
- 与混合信号方法相比,全数字CIM解决方案提供了更好的信号边缘和比特精度.
- 在NN中,低重精度 (<12b) 导致精度较低但功率效率更高; 8b输入精度满足实施需求.
- 基于SRAM的加速器的最大报告性能在330MHz时达到7.49TOPS,而基于SRAM的CIM在100MHz时达到5.6GOPS.
- 高清FINFET 6T-SRAM电池的读取访问时间比HC配置低32%,泄漏功率比HC配置好1.09倍.
- 在没有辅助系统的情况下,FinFET 6T-SRAM的最低可实现的电源电压为600mV,温度变化导致噪声边缘偏差高达22%.
结论:
- 数字信号域CIM,特别是使用6T-SRAM,是高性能AI硬件的可行方法.
- FinFET 6T-SRAM技术为人工智能计算系统提供了显著的性能优势,高清配置显示出卓越的效率.
- 仔细考虑精度,电压和环境因素对于优化基于FinFET 6T-SRAM的AI硬件至关重要.
关键词:
对于CIM加速器来说,这是非常重要的.FinFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-SRAM-FINFET-FINFET-SRAM-FINFET-SRAM-SRAM-SRAM.org.org.org.org.org.org.org.org.org.org.org.org.org.org.org.org.org.org.org.一个SRAM单元的细胞.在内存中进行计算.卷积神经网络 (CNN) 是一个神经网络.乘法和积累 (MAC) 的方法.
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