r/MachineLearning
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Noise-aware training for analog hardware: accuracy collapses at a threshold rather than degrading smoothly [D]
Analog in-memory compute is getting attention again as a way around the energy cost of moving weights between memory and compute. The recurring objection is noise, since analog cells have real variation and you can't refresh your way out of it like you can with digital. I wanted to see the shape of the degradation curv