A 64 Kb Reconfigurable Full-Precision Digital ReRAM-Based Compute-In-Memory for Artificial Intelligence Applications

This work presents a fully-digital 64 Kb non-volatile ReRAM based compute-in-memory (CIM) macro for the modern artificial intelligence (AI) edge devices, using 65 nm technology. This digital CIM architecture effectively removes the analog-design issues, related to process variations, noise susceptibility, and data-conversion overhead. Hence, it offers no accuracy loss and high energy-efficiency for the computation. To incorporate the digital computation, a novel NAND logic based 3.25T1R bitcell is proposed. The digital behaviour of this cell makes it superior to the conventional 1T1R based analog bitcell. Also, with the inherent non-volatility of ReRAM, the proposed cell can be a good substitute for SRAM-based CIM architectures with <tex-math notation="LaTeX">4.62× </tex-math> , <tex-math notation="LaTeX">1.96× </tex-math> , <tex-math notation="LaTeX">3.96× </tex-math> , and <tex-math notation="LaTeX">5.12× </tex-math> lower area than the XNOR-based 12T, Twin-8T, 8T, and 6T SRAM cell respectively. Moreover, the proposed CIM architecture allows full reconfigurabiliy from 1 to 16b precision for both input and weight. It also allows activating any number of parallel inputs, ranging from 1 to 128. According to simulation results, the proposed macro successfully operates up to 166.6 MHz for 1/8/15b input/weight/output precision and achieves 27.28 TOPS/W without any accuracy loss. Removing sense amplifiers for the ReRAM mode of the proposed work claims additional area and power savings.