Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications. Compared to current-domain CiM solutions, charge-domain CiM shows the opportunity for higher energy efficiency and resistance to device variations. However, the area occupation and standby leakage power of existing SRAMbased charge-domain CiM (CD-CiM) are high. This paper proposes the first concept and analysis of CD-CiM using nonvolatile memory (NVM) devices. The design implementation and performance evaluation are based on a proposed 2-transistor-1-capacitor (2T1C) CiM macro using ferroelectric field-effect-transistors (FeFETs), which is free from leakage power and much denser than the SRAM solution. With the supply voltage between 0.45V and 0.90V, operating frequency between 100MHz to 1.0GHz, binary neural network application simulations show over 47%, 60%, and 64% energy consumption reduction from existing SRAM-based CD-CiM, SRAM-based current-domain CiM, and RRAM-based current-domain CiM, respectively. For classifications in MNIST and CIFAR-10 data sets, the proposed FeFETbased CD-CiM achieves an accuracy over 95% and 80%, respectively.

中文翻译：

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利用铁电FET实现低功耗电荷域非易失性内存计算

内存计算（CiM）是缓解特定领域应用程序的内存墙问题的有前途的方法。与电流域CiM解决方案相比，电荷域CiM显示了更高的能效和抵抗器件变化的机会。但是，现有的基于SRAM的电荷域CiM（CD-CiM）的面积占用和待机泄漏功率很高。本文提出了使用非易失性存储器（NVM）设备的CD-CiM的第一个概念和分析。设计实现和性能评估基于使用铁电场效应晶体管（FeFET）的拟议的2晶体管1电容器（2T1C）CiM宏，该器件无泄漏功率，并且密度比SRAM解决方案高得多。电源电压在0.45V至0.90V之间，工作频率在100MHz至1.0GHz之间，二进制神经网络应用仿真显示，与现有的基于SRAM的CD-CiM，基于SRAM的电流域CiM和基于RRAM的电流域CiM相比，能耗分别降低了47％，60％和64％。对于MNIST和CIFAR-10数据集中的分类，建议的基于FeFET的CD-CiM的准确度分别超过95％和80％。