一种基于忆阻特性的监督神经网络算法及电路设计

A memristor-based supervised neural network algorithm and its circuit design

针对如何将忆阻器融入人工神经网络算法并进行硬件实现的问题,提出了一种在现场可编程逻辑门阵列(FPGA)平台上实现的基于忆阻特性的监督神经网络算法。该设计以忆阻器模块作为神经网络中的权值存储模块,构建误差反馈机制的监督学习。将该忆阻神经网络电路应用于图像分类问题,并进行了资源占用和处理速度的优化。实验结果表明其分类结果良好,在Cyclone Ⅱ:EP2C70F896I8平台上,整体网络算法占用11 773个逻辑单元(LEs),训练耗时0. 33 ms,图像的测试耗时10μs。这一工作对忆阻器和神经网络的结合提出了一个有益的参考。

This paper introduces a way to realize the supervised neural network algorithms based on memristive characteristics on Field Programmable Gate Array(FPGA)for the problem that how to take the memristors into artificial neural networks and hardware implement.This design uses memristors module as weight store module in neural network to build supervised learning with error feedback mechanism.The memristive neural networks are used in pattern recognition and their hardware resource and processing speed are optimized.Experiment results show that the performance of pattern recognition is quite good.Further,the hardware re-source occupancies and training time are 11 773 logic elements(LEs)and 0.33 ms on Cyclone II:EP2 C70 F896 I8,respectively,and the test time of images is 10μs,which gives a useful reference for combination of memristors and neural networks.