HE-FR: A High-Efficiency Fault-Resilient Framework for Stuck-at-Faults Mitigation in ReRAM-Based In-Memory Computing

Abstract

ReRAM-based in-memory computing (IMC) architectures enable efficient neural-network inference on edge devices, due to their high density and non-volatility. However, ReRAM is prone to the stuck-at faults (SAFs) which would distort weight mappings and reduce model accuracy substantially. To tackle this problem, a high-efficiency fault-resilient (HE-FR) framework is introduced in the paper, which couples layered-precision quantization based on the generalized gaussian distribution cumulative distribution function (GGD-CDF), preserving weight distributions with the minimal information loss. Further, a fault-aware weight re-decomposition method is proposed. Specifically, a Q-agent-based method is proposed for sparse SAFs, which employs offline reinforcement learning to construct a globally optimized hash table with reduced compilation complexity, while incorporating both weight matching and cell-state stability into the reward function to suppress electro-stress. For dense SAFs, we exploit a greedy search over the remaining cells to provide rapid and accurate mappings. Experimental results show that GGD-CDF quantization improves accuracy by 2.18% over Float32. Under fabrication faults, HE-FR surpasses a fault-free method by 12.97% and accelerates compilation by $197\times $ with only 0.024% picojoule-level energy overhead. In electro-stress evaluations, HE-FR reduces performance degradation by 46.27%. It demonstrates that the proposed framework has advantages in robustness, efficiency, and reliability.