10T SRAM单元作为大范围布尔计算的内存计算引擎

Abhash Kumar, Jawar Singh, B. Gupta
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引用次数: 0

摘要

在过去的几十年里,冯·诺伊曼计算体系结构一直是几乎所有计算系统的主力。然而,随着对数据密集型计算系统的需求不断增长,它面临着严重的内存墙问题,也被称为冯·诺伊曼瓶颈。为了缓解这一瓶颈,研究人员提出的一种方法是启用内存中的布尔计算。本文首次提出了在10个晶体管(10T) SRAM位元阵列内进行内存计算,以实现任意积和(SOP)形式的布尔逻辑。采用45nm PTM模型卡设计了10T SRAM单元,并在SPICE工具上进行了仿真。从蒙特卡罗分析中观察到,内存操作对过程变化是准确和稳健的。此外,与之前的工作相比,我们在实现任意布尔逻辑方面获得的收益掩盖了能量和延迟的微不足道的增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
10T SRAM Cell as an In-Memory Computing Engine for a Large Range of Boolean Computations
The von Neumann computing architecture has been the workhorse for virtually all computing systems for the last several decades. However, it faces serious issues of memory wall problems with the ever-increasing demand for data-intensive computing systems, also known as the von Neumann bottleneck. To mitigate this bottleneck, one of the approaches that researchers have come up with is to enable in-memory Boolean computation. In this paper, in-memory computing within ten transistors (10T) SRAM bit cell array to realize any arbitrary Boolean logic in sum-of-product (SOP) form is proposed for the first time. The proposed 10T SRAM cell was designed using 45nm PTM model cards and simulated on SPICE tool. The in-memory operations were found to be accurate and robust to process variations as was observed from Monte Carlo analysis. Further, the gain we get in terms of realization of arbitrary Boolean logic overshadows the negligible increase in energy and latency compared to previous works.
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