Quantization has been proven to be an effective method for reducing the computing and/or
storage cost of DNNs. However, the trade-off between the quantization bitwidth and final …

The world has seen the great success of deep neural networks (DNNs) in a massive number
of artificial intelligence (AI) applications. However, developing high-quality AI services to …

Quantization for Convolutional Neural Network (CNN) has shown significant progress with
the intention of reducing the cost of computation and storage with low-bitwidth data inputs …

Quantization has been proven to be a vital method for improving the inference efficiency of
deep neural networks (DNNs). However, it is still challenging to strike a good balance …

Quantization for CNN has shown significant progress with the intention of reducing the cost
of computation and storage with low-bitwidth data representations. There are, however, no …

The great success of artificial intelligence (AI) has been driven in part by the continuous
improvement of deep neural networks (DNNs) with deeper and more sophisticated model …

Deep Neural Networks (DNNs) have demonstrated remarkable performance in various
application domains, such as computer vision, pattern recognition, and natural language …

Deep Neural Networks (DNNs) have demonstrated remarkable performance in various
application domains, such as computer vision, pattern recognition, and natural language …

The rapidly growing machine learning development has demonstrated its great capability
and effectiveness in handling complicated real-world problems such as computer vision and …

The size and complexity of Neural Network models grow rapidly in recent years, which
makes the inference of these models require more computational and memory resources. To …