On-device neural network training for edge devices

Abstract

A processor-implemented method for a fixed-point, forward-forward on-device model training/adaptation is described. The processor-implemented method includes running a first forward call according to positive perturbation parameters sampled from a random perturbation vector that follows standard, normal distribution. The processor-implemented method also includes running a second forward call according to negative perturbation parameters sampled from the random perturbation vector. The processor-implemented method further includes computing forward gradients according to the random perturbation vector and a directional derivative based on the first forward call and the second forward call. The processor-implemented method also includes updating weights of the on-device model according to the forward gradients.

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