LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies

Autor:	Odema, Mohanad, Rashid, Nafiul, Demirel, Berken Utku, Faruque, Mohammad Abdullah Al
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Distributed Parallel and Cluster Computing Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them. Previous solutions proposed to distribute workloads at runtime according to the state of the surroundings, like the wireless conditions. However, such conditions are usually overlooked at design time. This paper addresses this issue for DNN architectural design by presenting a novel methodology, LENS, which administers multi-objective Neural Architecture Search (NAS) for two-tiered systems, where the performance objectives are refashioned to consider the wireless communication parameters. From our experimental search space, we demonstrate that LENS improves upon the traditional solution's Pareto set by 76.47% and 75% with respect to the energy and latency metrics, respectively. Comment: To appear at the 58th IEEE/ACM Design Automation Conference (DAC), December 2021, San Francisco, CA, USA
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.09309 Zobrazit plný text záznamu View this record from Arxiv