LAUSR

Computing-in-memory (CIM) is a new architecture which is more energy-efficient than the Von Neumann architecture due to the fact that it performs calculation in the memory units which can reduce a large amount of data movement. Nowadays, CIM with non-volatile memory (nvCIM), such as resistive random access memory (RRAM), has become a research frontier to further improve computing performance. Recent works mainly explored how to improve the computing performance of nvCIM, but seldom paid attention to the problem of accuracy loss. In this paper, we propose the nvCIM framework, which can systematically analyze the relationship between the classification accuracy of network models and main analog factors. Based on the nvCIM framework, we further provide detailed optimization methods, including RRAM features, array properties, and ADC parameters. The adaptive voltage-controlled SET and pulse-controlled RESET (VSPR) program-verify scheme is proposed to achieve high-resolution RRAM. And the margin enhancement based current-mode sense amplifier (MECSA) and offset reduction based analog-to-digital converter (ORADC) are proposed to improve the accuracy of analog part computing. Experimental results show that the macro-level and system-level energy efficiency is 112.1 TOPS/W and 9.86 TOPS/W respectively with less than 3.51% loss in contrast to the ideal accuracy, which is $2.9\times $ - $25.9\times $ compared to the energy efficiency of existing RRAM based nvCIM accelerators.