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Finding analog architectures for something that is largely continuous but quantized for digital circuitry right now (gradient descent) is pretty appealing. I’d
by vessenes 2y ago
Finding analog architectures for something that is largely continuous but quantized for digital circuitry right now (gradient descent) is pretty appealing. I’d love to see a toy network built out; I wonder how physically large this breadboard setup would have to be to get good results on MNIST, for instance.
- robxorb 2y agoAs I understand it, they were able to train and classify XOR using 32 of their breadboard nodes, which visually looks roughly a 1 metre square. XOR can usually be done with 1 or 2 neurons. And MNIST digit classification with ~25,000. Bringing all those numbers together we get 1 neuron = 32 nodes = 1sqm, would give a size of about a 900m square if breadboarded out for ordinary MNIST digits. (Assuming of course, no power transmission losses...!) I'm hoping, if not outright assuming, that I've made some kind of catastrophic error here.