As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

Quantization has become a popular technique to compress neural networks and reduce
compute cost, but most prior work focuses on studying quantization without changing the …

For efficient neural network inference, it is desirable to achieve state-of-the-art accuracy with
the simplest networks requiring the least computation, memory, and power. Quantizing …

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of
tasks, and deep learning holds the potential to advance a multitude of applications …

Convolutional neural networks (CNNs) play an important role in today's mobile and edge
computing systems for vision-based tasks like object classification and detection. However …

Model quantization can reduce the model size and computational latency, it has become an
essential technique for the deployment of deep neural networks on resourceconstrained …

In recent years, the lottery tickets hypothesis has gained widespread popularity as a means
of network compression. However, the practical application of lottery tickets for hardware …

Summary Mixed-Precision Quantization (MPQ) and Transprecision Computing (TC)
represent two valuable techniques used to optimize Deep Neural Networks (DNNs) …

隨著人工智能及其應用的快速發展, 神經網絡模型變得更加複雜, 架構的計算量和參數量更是
以往的數千倍以上. 而這也使得人工去搜尋或是建立架構變得極其困難. 也因此 …

As an effective technique to achieve the implementation of deep neural networks in edge
devices, model quantization has been successfully applied in many practical applications …