Variationaware Binarized Memristive Networks The quantization of weights to binary states in Deep Neural Networks (DNNs) can replace resourcehungry multiply accumulate operations with simple accumulations. Such Binarized Neural Networks (BNNs) exhibit greatly reduced resource and power requirements. In addition, memristors have been shown as promising synaptic weight elements in DNNs. In this paper, we propose and simulate novel Binarized Memristive Convolutional Neural Network (BMCNN) architectures employing hybrid weight and parameter representations. We train the proposed architectures offline and then map the trained parameters to our binarized memristive devices for inference. To take into account the variations in memristive devices, and to study their effect on the performance, we introduce variations in  R_textON  and  R_textOFF . Moreover, we introduce means to mitigate the adverse effect of memristive variations in our proposed networks. Finally, we benchmark our BMCNNs and variationaware BMCNNs using the MNIST dataset.

Fast and Lightweight Binarized Neural Network Implemented in an FPGA using LUTbased Signal Processing and its Timedomain Extension for Multibit Processing Fast and smallresourced implementation of convolutional neural network (CNN) into a fieldprogrammable gate array (FPGA) was realized using a binarized neural network (NN). We propose a set of neuron and network models optimized for fully binarized implementation of general NNs using the lookuptables (LUTs) in modern FPGAs, which is herein referred to as sparseLUT model. Arrayed MNIST data images of more than 40 characters input from a camera were recognized with 92.8% accuracy and classified with the colored marks on the characters on organic lightemitting diode (OLED) display images with 1ms cycle time, u003c1.0ms delay in the LUTbased CNN recognition, and u003c2ms total time delay. In combination with stochastic timedivided signal processing, binarized signals in this model can be extended for processing multibit (analoguelike) signals in an oversampling manner with increased recognition accuracies up to 98.6% in MNIST and 58.3% in CIFAR10 image data sets using CNN. The source codes for the binarized NN core were released and opensourced.