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Yun-Fan Yang

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Aug 2026

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...

H. Ding, Yun-Fan Yang, Zongwei Wang et al. · 0 citations

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