A RRAM-Based CIM Design With in-Situ Transposable Computing and Hybrid-Precision Scheme for Edge Learning

Abstract

Edge computing devices demand efficient transposable computing architectures to enable on-chip learning, necessitating novel hardware designs that balance performance and flexibility. We present an RRAM-based compute-in-memory macro capable of supporting both in-situ forward and backward propagation operations. The design incorporates an orthogonal-WL array structure with weight-level parallelism adjustment and a precision-driven input mechanism to enable flexible transposable computing. Additionally, optimized ADCs provide high throughput while maintaining area efficiency. The work exhibits a SOTA normalized area efficiency of 126.7 TOPS/mm2/bit, an energy efficiency of 2348.96 TOPS/W/bit, and a storage density of 4.84 Mb/mm2.