HLQ: Hardware-Friendly Logarithmic Quantization Aware Training for Power-Efficient Low-Precision CNN Models

With the development of deep learning and graphics processing units (GPUs), various convolutional neural network (CNN)-based computer vision studies have been conducted. Because numerous computations are involved in the inference and training process of CNNs, research on network compression, includi...

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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 159611 - 159621
Main Authors:	Choi, Dahun, Park, Juntae, Kim, Hyun
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Artificial neural networks Compression algorithms Computational modeling Computer vision convolutional neural network Convolutional neural networks Graphics processing units Hardware Indexes Integrated circuit modeling Logarithmic quantization Logarithms Low power electronics low-power Machine learning network compression Power demand Propagation losses Quantization (signal) Training
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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