RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge
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 energy), or trust a single chip blindly (cheap, but with no guarantee on how often it is wrong). T...
| Authors | Osama Yousuf, Martin Lueker-Boden |
|---|---|
| Primary category | cs.ET |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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