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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Bibliographic Details
Published in:2021 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED) p. 1
Main Author: Marculescu, Diana
Format: Conference Proceeding
Language:English
Published: IEEE 26.07.2021
Subjects:
Clouds
Energy efficiency
Hardware
hardware-aware ML
Machine learning
Measurement
Mobile handsets
model compression
neural architecture search
quantization
Runtime
Online Access:Get full text
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Summary: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 these metrics translate directly to energy efficiency, runtime, or mobile device battery lifetime. This work uncovers the need for building accurate, platform-specific power and latency models for ML and efficient hardware-aware ML design methodologies, thus allowing machine learners and hardware designers to identify not just the best accuracy ML model configuration, but also those that satisfy given hardware constraints.
DOI:10.1109/ISLPED52811.2021.9502472