FPGA Implementation of Binarized Perceptron Learning Hardware Using CMOS Invertible Logic This paper introduces FPGA implementation of learning hardware for a neural network. The proposed learning hardware is designed using CMOS invertible logic that realizes probabilistic bidirectional (forward and backward) operations with basic CMOS logic gates. The backward operation based on CMOS invertible logic makes hardwarebased learning possible because the loss function is not required. For a simple case study, the proposed learning hardware trains using simplified a MNIST data set for a 25input binarized perceptron. Our FPGA implementation on Digilent Genesys 2 achieves around 100 x faster operating speed than that using a traditional learning algorithm on software while maintaining the same recognition accuracy of 99%.

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.