News / Design & IP / Enabling Retraining-Free Accurate Inference in Analog Compute in Memory Accelerators
Enabling Retraining-Free Accurate Inference in Analog Compute in Memory Accelerators
By The Semiconductor Hub news desk ·
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Analog compute-in-memory (CIM) accelerators compute matrix-vector products directly inside memory arrays, eliminating weight movement and promising order-of-magnitude gains in inference efficiency. However, converter and device errors make pretrained models unusable on these arrays, and hardware-aware retraining is costly. We enable retraining-free, accurate inference of pretrained models on simple analog arrays with low-cost, shared calibration. Our key observation is that analog error depends…
Read at arXiv cs.AR (Hardware Architecture)Source: arXiv cs.AR (Hardware Architecture), published . The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern