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%.