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Would be interested to hear the take that more experienced ML practitioners had on this, but from skimming the Nature article (https://www.nature.com/articles/s
by nmstoker 4y ago
Would be interested to hear the take that more experienced ML practitioners had on this, but from skimming the Nature article (https://www.nature.com/articles/s41586-022-04992-8 https://www.nature.com/articles/s41586-022-04992-8) it seems like there is some reconfigurability built into these chips but overall they're at a fair early stage. It doesn't yet sound like it's something one could take a model from one of the well known frameworks and simply drop it in / automatically convert it for use in these NeuRRAM chips but presumably something along those lines might be on the cards as things mature.
- visarga 4y agoThe article says: > They found that it’s 99% accurate in letter recognition from the MNIST dataset, 85.7% accurate on image classification from the CIFAR-10 dataset, 84.7% accurate on Google speech command recognition and showed a 70% reduction in image-reconstruction error on a Bayesian image recovery task. These are all small scale models that run on very low power, on edge devices. Not gonna load GPT-3 on it soon. It's for wake word detection and security cameras.