REF-CIM: A 40-nm Non-Ideality Tolerant and Energy Efficient RRAM Compute-in-Memory Macro With Configurable Precision for Edge AI
Compute-in-Memory (CIM) based on resistive random access memory (RRAM) offers significant advantages in energy efficiency and parallelism, making it a promising solution for accelerating neural networks. However, the computational accuracy, energy efficiency, and flexibility of current CIM chips are still challenged by...