Memristive neuromorphic computing approach combining calibration method and in-memory training

Memristor based neuromorphic computing architecture has achieved good results in image classification, speech recognition and other fields, but when the memristor array has the problem of low yield, the performance declines significantly. A method combining memristive neuromorphic computing based calibration method with in-situ training was proposed, which increased the accuracy of multiplicative accumulation calculation by using the calibration method and reduced the training error by using the in-situ training method. In order to verify the performance of the proposed method, a multi-layer perceptron architecture was used for simulation. From the simulation results, the accuracy of the neural network is improved obviously (nearly 40%). Experimental results show that compared with the single calibration method, the precision of the network trained by the proposed method is improved by about 30%, and the precision of the network trained by the proposed method is improved by 0.29% when compared with other mainstream methods.