The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Abstract Model binarization is an effective method of compressing neural networks and
accelerating their inference process, which enables state-of-the-art models to run on …

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are
both binarized into 1-bit values, thus greatly reducing the memory usage and computational …

Network binarization is a promising hardware-aware direction for creating efficient deep
models. Despite its memory and computational advantages, reducing the accuracy gap …

Binary Neural Networks (BNNs) show promising progress in reducing computational and
memory costs but suffer from substantial accuracy degradation compared to their real …

Abstract We introduce Larq Compute Engine (LCE), a state-of-the-art Binarized Neural
Network (BNN) inference engine, and use this framework to investigate several important …

Abstract Binary Neural Networks (BNNs) show promising progress in reducing
computational and memory costs, but suffer from substantial accuracy degradation …

This paper formalizes the binarization operations over neural networks from a learning
perspective. In contrast to classical hand crafted rules (\eg hard thresholding) to binarize full …

Binary neural networks (BNNs) have received increasing attention due to their superior
reductions of computation and memory. Most existing works focus on either lessening the …

Binarization of neural network models is considered as one of the promising methods to
deploy deep neural network models on resource-constrained environments such as mobile …