A Precision-Adaptive ECC Strategy with Computing Fusion Decoding for Near/In-Memory Computing
Pufan Xu, Peng Yao, Bin Gao, Jianshi Tang, He Qian, Huaqiang Wu · 2025
With the rapid growth of data amount in artificial intelligence (AI), near-memory computing (NMC) and in-memory computing (IMC) have emerged as promising solutions to overcome the "memory wall". Traditional storage-centric error-correcting code (ECC) schemes perform error correction on arbitrary bits, introducing significant area and energy overhead. Such bit-level ECC is unnecessary for NMC/IMC architectures, as AI workloads prioritize overall computational accuracy over exact bit correctness. While recent IMC ECC methods focus on computational results rather than bit-level precision, they still lack adaptivity to meet the dynamic precision requirements of AI workloads and the fixed code length results in high area and power overhead.To address these challenges, this paper proposes a precision-adaptive ECC strategy for NMC/IMC for the first time. Our approach dynamically adjusts the encoded data size based on the target workload’s precision requirements and employs an orthogonal compression method to lessen the number of parity bits. By fusing the decoding process into the computational features of NMC/IMC, we reuse existing computational circuits to minimize overhead. Experimental results demonstrate that the proposed method achieves a 45.24% improvement in energy efficiency and a 12.04% reduction in area compared to traditional ECC schemes.