A Green Granular Convolutional Neural Network with Software-FPGA Co-Designed Learning

Abstract

Different from traditional tedious CPU-GPU-based training algorithms using gradient descent methods, the software-FPGA co-designed learning algorithm is created to quickly solve a system of linear equations to directly calculate optimal values of hyperparameters of the green granular neural network (GGNN). To reduce both $CO_2$ emissions and energy consumption effectively, a novel green granular convolutional neural network (GGCNN) is developed by using a new classifier that uses GGNNs as building blocks with new fast software-FPGA co-designed learning. Initial simulation results indicates that the FPGA equation solver code ran faster than the Python equation solver code. Therefore, implementing the GGCNN with software-FPGA co-designed learning is feasible. In the future, The GGCNN will be evaluated by comparing with a convolutional neural network (CNN) with the traditional software-CPU-GPU-based learning in terms of speeds, model sizes, accuracy, $CO_2$ emissions and energy consumption by using popular datasets. New algorithms will be created to divide the inputs to different input groups that will be used to build different small-size GGNNs to solve the curse of dimensionality.