Fig. 4
From: Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
Analysis based on experimental-calibrated simulation. a The experimental classification error (accuracy shown in Supplementary Fig. 10) matches the simulated accuracy. The simulation considers experiment parameters, including 11% devices stuck at 10 μS, 2% conductance update variation, limited conductance dynamic range, etc. The simulation on defect-free assumption shows an accuracy approaching that from TensorFlow. Each data point is the classification error rate on the complete testing set (10 000 images) after 500 images (simulation or TensorFlow) or 5000 images (experiment). b The impact of non-responsive devices on the inference accuracy with in situ and ex situ training approach. The non-responsive device was stuck in a very low-conductance state (10 µS), which is the typical defect device value observed in the experiment. The result shows that the in situ training process adapts to the defects, providing a much higher defect tolerance compared with pre-loading ex situ training weights into the network. With 50% stuck OFF devices, the network can still achieve over 60% accuracy. The error bar shows the s.d. over 10 simulations. c The multilayer network also helps with the defect tolerance. If one device is stuck, the associated hidden neuron will adjust the connections accordingly. The error bar shows the s.d. over 10 simulations. d The simulation of a larger network constructed on a larger memristor crossbar (1024 × 512) with experimental parameters (e.g., 11% defect rate) could achieve accuracy above 97%, which suggests a large memristor network could narrow the accuracy performance gap from the conventional CMOS hardware. The network architecture is shown in Supplementary Fig. 14
