Extended Data Fig. 3: Drift of conductance weights in time and associated degradation in system accuracy.
From: Fully hardware-implemented memristor convolutional neural network
a, Changes in the conductance weights with time, over 30 days after the transfer. The grey lines present the changing traces of all the cell weights, and the three coloured lines depict representative evolution trends. b, Mean weight value for the cells that belong to each of the 15 levels according to a. The 15 coloured traces show the 15 mean-value evolution traces as a function of time. c, Profile of accuracy loss during the experiment. The overall trend of the accuracy loss indicates how the conductance weight drifts deteriorate the recognition accuracy over time after hybrid training. Compared with the initial state, the recognition accuracy increases by 0.37% at t = 10 min, owing to random device-state fluctuations. d, Evolution of the weights of the weight cells considered in c over 2 h. t0 denotes the moment when the hybrid training is completed. The grey lines show the changing traces of the states of the cells, and the three coloured lines depict representative evolution trends.
