Training of artificial neural networks

Abstract

Methods and apparatus are provided for training an artificial neural network having a succession of neuron layers with interposed synaptic layers each having a respective set of N-bit fixed-point weights {w} for weighting signals propagated between its adjacent neuron layers, via an iterative cycle of signal propagation and weight-update calculation operations. Such a method includes, for each synaptic layer, storing a plurality p of the least-significant bits of each N-bit weight w in digital memory, and storing the next n-bit portion of each weight w in an analog multiply-accumulate unit comprising an array of digital memory elements. Each digital memory element comprises n binary memory cells for storing respective bits of the n-bit portion of a weight, where n ≥ 1 and (p + n + m) = N where m ≥ 0 corresponds to a defined number of most-significant zero bits in weights of the synaptic layer.