Extended Data Fig. 6: Scalability of the joint strategy.

The joint strategy combines the hybrid training method and the parallel computing technique of replicating the same kernels. We show that a small subset of training data is sufficient for hybrid training. a, Recognition accuracies at different stages of the simulation process. During the simulation with ResNET-56, the kernel weights of the first convolutional layer are replicated to four groups of memristor arrays. b, After hybrid training the error rate on the test set drops substantially compared with that obtained immediately after weight transfer using each convolver group. c, The error rates drop considerably after hybrid training using 10% of the training data in the experiment with the five-layer CNN.The three experimental results show good consistency. d, Recognition accuracies at different stages of the simulation with ResNET-56. A high level of accuracy is achieved even when using 3% of the training data (1,500 training images) to update the weights of the FC layer. The mean accuracy for 10 trials is 92.00% after hybrid training, and the standard deviation is 0.8%.