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RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Analog in-memory computing (AIMC) speeds up neural-network inference by doing the arithmetic directly inside a memory array, instead of shuttling weights back and forth between memory and a processor. This saves energy, but the physical devices that store the weights are imperfect: programming errors, electrical noise, limited-resolution converters, and outright broken cells all distort the computation, and every physical chip is distorted in its own way. A designer with several such chips available faces an uncomfortable choice: run all of them and combine the answers (safe, but wasteful of e

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