A High-Parallelism RRAM-Based Compute-In-Memory Macro With Intrinsic Impedance Boosting and In-ADC Computing

Abstract

Resistive random access memory (RRAM) is considered to be a promising compute-in-memory (CIM) platform; however, they tend to lose energy efficiency quickly in high-throughput and high-resolution cases. Instead of using access transistors as switches, this work explores their analog characteristics as common-gate current buffers. So the cell current can be minimized and the output impedance is boosted. The idea of In-ADC Computing (IAC) is also proposed to further decrease the complexity of the peripheral circuits. Benefiting from the proposed ideas, a pretrained VGG-8 network based on the CIFAR-10 dataset can be implemented, and an accuracy of 87.2% is achieved with 8.9 TOPS/W energy efficiency (for 8-bit multiply-and-accumulate (MAC) operation), demonstrating that the proposed techniques enable low-distortion partial sum results while still being able to operate in a power-efficient way.