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Simulation of a Fully Digital Computing-in-Memory for Non-Volatile Memory for Artificial Intelligence Edge
Hongyang Hu1,2, Chuancai Feng3, Haiyang Zhou1,2
1State Key Laboratory of Fabrication Technologies for Integrated Circuits, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.
Abstract:
In recent years, digital computing in memory (CIM) has been an efficient and high-performance solution in artificial intelligence (AI) edge inference. Nevertheless, digital CIM based on non-volatile memory (NVM) is less discussed for the sophisticated intrinsic physical and electrical behavior of non-volatile devices. In this paper, we propose a fully digital non-volatile CIM (DNV-CIM) macro with compressed coding look-up table (LUT) multiplier (CCLUTM) using the 40 nm technology, which is highly compatible with the standard commodity NOR Flash memory. We also provide a continuous accumulation scheme for machine learning applications. When applied to a modified ResNet18 network trained under the CIFAR-10 dataset, the simulations indicate that the proposed CCLUTM-based DNV-CIM can achieve a peak energy efficiency of 75.18 TOPS/W with 4-bit multiplication and accumulation (MAC) operations.
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