Fig. 3 | Nature Communications

Fig. 3

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

Fig. 3

In situ online training and inference experiments on Modified National Institute of Standards and Technology (MNIST) handwritten digit recognition. a Typical handwritten digits from the MNIST database. b Photo of the integrated 128 × 64 array during measurement. The array was partitioned into two parts for the first and second layers, respectively. In all, 54 hidden neurons were used, so the first layer weight matrix is 64 × 54 (implemented using 6912 memristors) and the second layer matrix is 54 × 10 (implemented using 1080 memristors). The blue and green false-colored areas are the positive and negative parts of the differential pairs. c Minibatch accuracy increases over the course of training. Experimental data followed the defect-free simulation closely, with a consistent 2–4% gap. d The conductance-gate voltage relation extracted from data collected during training. The conductance was read using the scheme described in the Methods. The conductance includes the effects of sneak-paths and wire resistance, which makes the measured values smaller and the variance larger than those in Fig. 1b, c. The dashed line indicates the mean conductance, while the error bars show a 95% confidence interval for the measured conductance. The real-time online training accuracy with the readout weight values is shown in an animation in Supplementary Movie 1. eg Typical correctly classified digit “9” and hj misclassified digit “8” after the in situ training. e, h Images of the actual digits from the MNIST test set used as the input to the network. f, i The raw current measured from the output layer neurons. The neuron representing the digit “9” has the highest output current, indicating a correct classification. g, j The corresponding Bayesian probability of each digit, as calculated by a softmax function. More inference samples are shown in Supplementary Fig. 7 and Supplementary Movies 2 and 3

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