使用 2 位 FeFET 单元的高效电荷域内存计算 1F1C 宏用于 DNN 处理

IF 2 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Nellie Laleni;Franz Müller;Gonzalo Cuñarro;Thomas Kämpfe;Taekwang Jang
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引用次数: 0

摘要

本文介绍的 1FeFET-1Capacitance (1F1C) 宏基于在电荷域工作的 2 位铁电场效应晶体管 (FeFET) 单元,标志着非易失性存储器 (NVM) 和内存计算 (CIM) 领域的重大进展。传统上,非易失性存储器(如场效应晶体管或电阻式 RAM (RRAM))以单比特方式运行,限制了其计算密度和吞吐量。相比之下,所提出的 2 位 FeFET 单元可实现更高的存储密度,并提高 CIM 架构的计算效率。该宏实现了 111.6 TOPS/W,突出了其能效,并在 CIFAR-10 数据集上表现出强劲的性能,使用 VGG-8 神经网络实现了 89% 的准确率。这些发现凸显了电荷域多级 NVM 单元在推动人工智能 (AI) 加速和高能效计算方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A High-Efficiency Charge-Domain Compute-in-Memory 1F1C Macro Using 2-bit FeFET Cells for DNN Processing
This article introduces a 1FeFET-1Capacitance (1F1C) macro based on a 2-bit ferroelectric field-effect transistor (FeFET) cell operating in the charge domain, marking a significant advancement in nonvolatile memory (NVM) and compute-in-memory (CIM). Traditionally, NVMs, such as FeFETs or resistive RAMs (RRAMs), have operated in a single-bit fashion, limiting their computational density and throughput. In contrast, the proposed 2-bit FeFET cell enables higher storage density and improves the computational efficiency in CIM architectures. The macro achieves 111.6 TOPS/W, highlighting its energy efficiency, and demonstrates robust performance on the CIFAR-10 dataset, achieving 89% accuracy with a VGG-8 neural network. These findings underscore the potential of charge-domain, multilevel NVM cells in pushing the boundaries of artificial intelligence (AI) acceleration and energy-efficient computing.
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来源期刊
CiteScore
5.00
自引率
4.20%
发文量
11
审稿时长
13 weeks
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