校准方法和存内训练相结合的忆阻器神经形态计算方法

基于忆阻器的神经形态计算架构在图像分类、语音识别等领域取得了较好的效果,但当忆阻器阵列存在低良率问题时,其性能会出现明显下降。提出一种基于忆阻器神经形态计算的校准方法和原位训练相结合的算法,利用校准方法提高乘累加计算的准确率,并利用原位训练方法降低训练误差。为了验证所提方法的性能,采用多层感知器架构进行仿真。从仿真结果来看,神经网络的精度有明显的提高(近40%)。实验结果表明,与单纯的校准方法相比,采用所提方法训练的网络精度提高了约30%,与其他主流的方法相比,所提方法训练的网络精度提高了0.29%。

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.