Trust, but Verify: Reliable Compute-in-Memory via Double-Reference Sensing and Selective Recompute

Compute-in-Memory (CiM) architectures based on non-volatile memories (NVMs) promise substantial performance and energy benefits by mitigating the memory wall through near-memory, massively parallel computation. However, NVM-CiM designs suffer from intrinsic reliability challenges caused by process variations, analog sensing of resistive states, imperfect reference levels, and reduced sense margins as operand count increases. These effects lead to overlapping output distributions and decision failures at the sense amplifier level, which accumulate across large applications and undermine end-to-end correctness. In this work, we propose a software–hardware co-design approach to ensure application-level reliability in NVM-CiM systems. Our method employs double-reference sensing to flag potential decision failures during CiM execution and records potential errors using a lightweight error-flag mechanism. Flagged outputs are selectively recomputed on the CPU, correcting errors while preserving the benefits of CiM execution. We quantify decision failure probabilities for different sense amplifier designs, study optimal reference placement, and quantify application-level reliability, comparing CiM execution with a CPU-only baseline. ... mehrResults show that by optimizing the double referencing design, fewer than 1% of outputs require correction on average, achieving application-level decision failure rates below 10−12. Despite introducing about 1% runtime and energy overhead, the proposed method remains significantly more efficient than CPU-only execution, enabling reliable and practical deployment of NVM-CiM architectures.