Adaptive loss-aware quantization for multi-bit networks

• Main Authors: QU, Zhongnan, ZHOU, Zimu, CHENG, Yun, THIELE, Lothar
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Quantization (signal); Optimization; Neural networks; Adaptive systems; Microprocessors; Training; Tensile stress; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5251; https://ink.library.smu.edu.sg/context/sis_research/article/6254/viewcontent/cvpr20_qu.pdf
• Institution: Singapore Management University

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adapti...