When Climate Meets Machine Learning: Edge to Cloud ML Energy Efficiency

A large portion of current cloud and edge workloads feature Machine Learning (ML) tasks, thereby requiring a deep understanding of their energy efficiency. While the holy grail for judging the quality of a ML model has largely been testing accuracy, and only recently its resource usage, neither of t...

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Bibliographische Detailangaben

Veröffentlicht in:	2021 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED) S. 1
1. Verfasser:	Marculescu, Diana
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 26.07.2021
Schlagworte:	Clouds Energy efficiency Hardware hardware-aware ML Machine learning Measurement Mobile handsets model compression neural architecture search quantization Runtime
Online-Zugang:	Volltext
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