Binarizing Weights Wisely for Edge Intelligence: Guide for Partial Binarization of Deconvolution-Based Generators

Liu, Jinglan; Zhang, Jiaxin; Ding, Yan; Xu, Xiaowei; Jiang, Meng; Shi, Yiyu

doi:10.1109/tcad.2020.2983370

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“…Liu et al [ 55 ] propose assessing the degree of redundancy of each layer before applying binarization since its use on layers with a higher degree of redundancy will ultimately lead to lower performance loss, while layers with a negative degree of redundancy should be kept un-binarized.…”

Section: Answering the Rqsmentioning

confidence: 99%

A Systematic Literature Review on Distributed Machine Learning in Edge Computing

Filho

Marques

Chang

et al. 2022

Sensors

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Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

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Section: Answering the Rqsmentioning

confidence: 99%