In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks
that use binary values for activations and weights, instead of full precision values. With …

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 …

Weight and activation binarization is an effective approach to deep neural network
compression and can accelerate the inference by leveraging bitwise operations. Although …

Abstract Binary Neural Network (BNN) shows its predominance in reducing the complexity of
deep neural networks. However, it suffers severe performance degradation. One of the …

Binarization of neural networks is a dominant paradigm in neural networks compression.
The pioneering work BinaryConnect uses Straight Through Estimator (STE) to mimic the …

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is
motivating the search for high-performance and efficient spiking neural networks to run on …

Binary neural networks (BNNs) represent original full-precision weights and activations into
1-bit with sign function. Since the gradient of the conventional sign function is almost zero …

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 …

Relying on the premise that the performance of a binary neural network can be largely
restored with eliminated quantization error between full-precision weight vectors and their …

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 …