Experiment Reveals Analog Hardware Noise Causes Threshold Accuracy Collapse
Georgiou1226 · reddit · 2026-08-09
Analog in-memory compute is regaining attention for reducing energy costs, but it faces challenges from physical noise in analog cells. The author conducted an experiment to investigate the impact of noise on model accuracy.
- Findings: The degradation of accuracy is not smooth but exhibits a threshold effect. Accuracy remains stable until weight noise reaches a specific point, after which it drops precipitously (from 83% to 64%, and eventually to random guessing).
- Noise Training: Injecting noise during training (forcing the optimizer to find flatter minima) significantly shifts this collapse threshold. At matched noise levels, the noise-trained model achieved 61% accuracy compared to 39% for standard training.
The author calls for community discussion on whether "flat minima" is the correct framing and if there are explicit optimization methods targeting the hardware's actual noise profile.
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