TentacleNet: A Pseudo Ensemble Template for Accurate Binary Convolutional Neural Networks
Binarization is an attractive strategy for implementing lightweight Deep Convolutional Neural Networks (CNNs) Despite the unquestionable savings offered, memory footprint above all, it may induce an excessive accuracy loss that prevents a widespread use. This work elaborates on this aspect introducing TentacleNet, a new template designed to improve the predictive performance of binarized CNNs via parallelization. Inspired by the ensemble learning theory, it consists of a compact topology that is end to end trainable and organized to minimize memory utilization. Experimental results collected over three realistic benchmarks show TentacleNet fills the gap left by classical binary models, ensuring substantial memory savings w.r.t. state of the art binary ensemble methods.
2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)