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

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
Main Authors: Dahun Choi, Juntae Park, Hyun Kim
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Logarithmic quantization
convolutional neural network
low-power
network compression
Online Access:https://ieeexplore.ieee.org/document/10738813/
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https://ieeexplore.ieee.org/document/10738813/

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