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Local learning with imperfect analog synapses
Analog in-memory computing delivers efficient neural network inference at the edge, but systems deployed in non-stationary environments may need to adapt in situ to locally acquired data. Backpropagation faces two structural obstacles here. Its global error transport lets stochastic write errors com
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