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Intermittent Inference with Nonuniformly Compressed Multi-Exit Neural Network for Energy Harvesting Powered Devices [article]

Yawen Wu, Zhepeng Wang, Zhenge Jia, Yiyu Shi, Jingtong Hu
2020 arXiv   pre-print
deploy multi-exit neural networks to EH-powered microcontrollers (MCUs) and select exits during execution according to available energy.  ...  To eliminate the indefinite long wait to accumulate energy for one inference and to optimize the accuracy, we developed a power trace-aware and exit-guided network compression algorithm to compress and  ...  This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided.  ... 
arXiv:2004.11293v2 fatcat:2mgwmlakubg2pafcu5bvfy6y7a

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey [article]

Xiaofei Wang and Yiwen Han and Victor C.M. Leung and Dusit Niyato and Xueqiang Yan and Xu Chen
2019 arXiv   pre-print
Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the  ...  cloud to the edge of the network.  ...  IoT edge environments powered by Energy Harvesting (EH) is investigated in [251] , [254] .  ... 
arXiv:1907.08349v2 fatcat:4hfqgdto4fhvlguwfjxuz3ik5q