Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Hardware-friendly network quantization (eg, binary/uniform quantization) can efficiently
accelerate the inference and meanwhile reduce memory consumption of the deep neural …

Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. To address this cost, a number of quantization schemes have been …

Reducing bit-widths of activations and weights of deep networks makes it efficient to
compute and store them in memory, which is crucial in their deployments to resource-limited …

Deep learning algorithms achieve high classification accuracy at the expense of significant
computation cost. In order to reduce this cost, several quantization schemes have gained …

In recent years, the fields of natural language processing (NLP) and information retrieval (IR)
have made tremendous progress thanks to deep learning models like Recurrent Neural …

Deep neural networks with adaptive configurations have gained increasing attention due to
the instant and flexible deployment of these models on platforms with different resource …

Owing to the presence of large values, which we call outliers, conventional methods of
quantization fail to achieve significantly low precision, eg, four bits, for very deep neural …