Modeling of Convolutional Neu r al Network Circuits for Human Activity Recognition

Neural computing capabilities has been studied by neuromorphic neural network circuit modeling in this work. A non- overlapped implanted (NOI) non-volatile memory device is used. The NOI array is designed to form the synapses of a convolutional neural network. To verify the network, recognition rates on six kinds of human activity are investigated. There are 12,000 data in training set and 3,000 data in test set. Convolution neural network is first trained in software. The recognition rates are 100% in training set and 98% in test set. The hardware simulation result using circuit/device models on the same database is 89% in test set.