Fig. 2 | Nature Communications

Fig. 2

From: Efficient and self-adaptive in-situ learning in multilayer memristor neural networks

Fig. 2

In situ training algorithm. a Schematic diagram of a two-layer neural network. Each neuron computes a weighted sum of its inputs and applies a nonlinear activation function. b The implementation of the network with a set of memristor crossbars. Each synaptic weight (arrows in a) corresponds to the conductance difference between two memristors (as illustrated by the orange columns). Each crossbar computes weighted sums of its input voltages. Between the crossbars is a layer of circuits that read the current from each wire, convert it to a voltage, and apply the activation function. The activation function was implemented in software in this work. c Flow chart of the in situ training. Steps in green boxes were implemented in hardware in this work, while those in yellow boxes were computationally expensive steps that can be accomplished with circuits integrated onto the chip in the future. The algorithm is described in detail in Methods

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