计算机科学
深度学习
深层神经网络
边缘设备
人工智能
修剪
人工神经网络
量化(信号处理)
机器学习
建筑
GSM演进的增强数据速率
领域(数学)
计算机体系结构
算法
云计算
艺术
数学
纯数学
农学
视觉艺术
生物
操作系统
作者
Ching-Hao Wang,Kang-Yang Huang,Yi Yao,Jun-Cheng Chen,Hong-Han Shuai,Wen-Huang Cheng
出处
期刊:IEEE Consumer Electronics Magazine
[Institute of Electrical and Electronics Engineers]
日期:2022-06-21
卷期号:13 (4): 51-64
被引量:39
标识
DOI:10.1109/mce.2022.3181759
摘要
With the recent success of the deep neural networks (DNNs) in the field of artificial intelligence, the urge of deploying DNNs has drawn tremendous attention because it can benefit a wide range of applications on edge or embedded devices. Lightweight deep learning (DL) indicates the procedures of compressing DNN models into more compact ones, which are suitable to be executed on edge devices due to their limited resources and computational capabilities while maintaining comparable performance as the original. Currently, the approaches of model compression include but not limited to network pruning, quantization, knowledge distillation, neural architecture search. In this work, we present a fresh overview to summarize recent development and challenges for model compression.
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