ReSCIM: Variation-Resilient High Weight-Loading Bandwidth In-Memory Computation Based on Fine-Grained Hybrid Integration of Multi-Level ReRAM and SRAM Cells

SRAM-CIM is a promising approach to implement efficient accelerator architecture as it enables accurate, energy-efficient AI computing, supporting both analog and digital computation. However, it has low area efficiency. On the other hand, Resistive RAM (ReRAM) provides dense on-chip storage, especially with multi-level cells (MLC), but ReRAM-CIM may introduce inaccuracies due to device variation and only supports analog computation. To leverage the strengths of both technologies, a hybrid architecture that combines them at a fine granularity is desirable. Previous hybrid designs incorporate ReRAM resistors into SRAM to improve storage density. However, they face scalability limitations and restricted signal margins for multi-level RRAM readout, leading to degraded computation accuracy. In this work, we propose ReSCIM, a hybrid compute-in-memory (CIM) architecture that seamlessly integrates multi-level ReRAM into SRAM cells at a fine-grained level. By incorporating a compact ReRAM crossbar in each SRAM cell, a dense CIM marco using SRAM-based computation is achieved. We develop an energy-efficient differential sensing scheme that enables parallel weight loading from local ReRAM crossbars to SRAM cells. This scheme allows multi-bit ReRAM data readout using a single SRAM cell and offers resilience to device variations. Furthermore, We designed a ReSCIM accelerator architecture for efficient AI acceleration, fully utilizing the highly scalable storage and exceptional weight-loading bandwidth. We employ a folded weight-mapping approach for MLC ReRAM cells to guarantee accurate classification even under substantial ReRAM device variations. Experimental results show that ReSCIM accelerators based on both analog and digital-based CIM achieve 60% energy savings and 98% latency savings, and 59× higher area efficiency compared to state-of-the-art all-weights-on-chip AI accelerators on AlexNet.