Fig. 5: Image recognition using CNNs with different precisions. | Nature Communications

Fig. 5: Image recognition using CNNs with different precisions.

From: 8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks

Fig. 5

a Schematic of a large-scale GIM 2D FGM array for vector-matrix multiplication in neural networks. b Schematic of CNNs for ImageNet image recognition. c, d Comparison of top-5 accuracy for CNNs quantized with different precisions after (c) and during (d) training. The quantization process used the nearest rounding scheme. The numbers of parameters are denoted in the brackets following the models’ names (4.3 million of MobileNet, 22.9 million of Xception).

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