Fig. 4: Fully hardware-based demonstration of a single-layer neural network for MNIST data classification and investigation of the impact of the defectiveness of the CA on the classification accuracy. | Nature Communications

Fig. 4: Fully hardware-based demonstration of a single-layer neural network for MNIST data classification and investigation of the impact of the defectiveness of the CA on the classification accuracy.

From: Purely self-rectifying memristor-based passive crossbar array for artificial neural network accelerators

Fig. 4

a Trained weight-mapping results of the D0 CA. After the training process, all cells in the CA were read out at a 2 V reading voltage. b VMM operation results of the D0 CA focusing on three parameters: VMM operation results (red bars), trained weight summations (green circles), and simulated weight summations (blue circles). The discrepancy between the VMM operation results and calculated values indicates the feasibility of the VMM operation, while the difference between the VMM operation results and the simulated values indicates the training accuracy. c Experimental demonstration of the fully hardware-based classification of the MNIST data using the D0 CA. One of the classification results was showcased by following the max-current sensing rule. The classification result is indicated by the blue bars, which represent the maximum values of the sensed signals. d Trained weight-mapping results, (e) VMM results focusing on the three parameters described in (b), and (f) representative classification results of the D30 CA. While the digits 0 and 2 were classified correctly, the digit 1 was misclassified as 2. g Trained weight-mapping results, h VMM results focusing on the three parameters described in (b), and (i) representative classification results of the D50 CA. j Classification accuracy for each digit based on 1500 classifications. The classification accuracy of the D0, D30, and D50 CAs for each digit is shown from left to right. k Classification accuracies for each digit in different defective CAs and the total classification accuracy. The total accuracies for each defective CA indicated that 100% accuracy was achieved in the D0 CA. However, the accuracies decreased to 68.5% in the D30 CA and 49.8% in the D50 CA. These results demonstrate the practical impact of defects in the CA, particularly open-circuit defects, which significantly degrade the classification accuracy.

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