高能效可编程模拟计算:标准 CMOS 工艺中的模拟计算

Jennifer Hasler
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摘要

20 世纪 80 年代,当我开始研究神经网络系统的模拟计算时,每个人都害怕在演讲结束时被问到这样一个问题:"难道这不能在 DSP 处理器上实现吗?卡弗-米德(Carver Mead)在 1990 年给出了一个理论和直观的答案,即模拟计算的效率应该是数字计算的数千倍。三十年后的今天,"难道这不能在 DSP 上实现吗?"的问题,或者与之相关的 "难道这不能在数字计算集成电路上实现吗?"的问题,已经得到了明确的解决,并在一些实施方案中得到了响亮的否定。本文论述了端到端模拟计算系统如何展示出比数字计算系统更高的效率。这些计算完全可以在标准 CMOS 工艺中使用任何 CMOS 集成电路工艺中的浮栅(FG)器件来实现。这种效率既体现在计算电路层面,也体现在架构层面。30 年前,首次出现了横杆和单晶体管突触概念。20 年前,米德的能效假设得到了最终验证,而在此之前,内存计算 (CiM) 以及支持 FG 连接的大规模 FPAA 也已开始应用。标准 CMOS 集成电路工艺中的模拟计算机会可以为更广泛的商业市场提供许多机会。
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Energy-Efficient Programable Analog Computing: Analog computing in a standard CMOS process
When I started working on analog computing for neural network systems in the 1980s, the question everyone feared to be asked at the end of their presentation was “couldn’t this be done on a DSP processor?” Carver Mead provided a theoretical and intuitive answer in 1990 that analog computing should be thousands of times more efficient than digital computation. Three decades later the “couldn’t this be done on a DSP?” question, or its related question “couldn’t this be done on a digital computing IC?”, has been definitively addressed with a resounding No for several implementations. This paper addresses how end-to-end analog computing system demonstrates this increased efficiency over digital computing systems. These computations are possible entirely in a standard CMOS process using Floating-Gate (FG) devices available in any CMOS IC process. The efficiency is both at the computational circuit level, as well as at the architecture level. The first crossbar and single-transistor synapse concept appeared 30 years ago. The definitive demonstration of Mead’s energy efficiency hypothesis appeared 20 years ago, and before that demonstration was the start of Compute in Memory (CiM) as well as the start of a large-scale FPAA with FG-enabled connections. Analog computing opportunities in Standard CMOS IC processes can provide many opportunities for the wider commercial market.
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