Energy Harvesting-Supported Efficient Low-Power ML Processing with Adaptive Checkpointing and Intermittent Computing
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Abstract
The rise of ultra-low-power embedded processors has led to increased use of energy harvesting devices (EHDs), providing portability and extended lifespans, but also presenting challenges due to sporadic ambient energy and limited storage. This paper introduces "Micro-Controller Unit - Early Exit Neural Network" MCU-EENet, a framework utilizing kinetic energy harvesting to support machine learning tasks intermittently with early exits. The intermittent nature of ambient energy can lead to potential program interruptions, necessitating efficient state retention techniques within MCU-EENet. We propose "SmartCheck," a memory-optimized runtime checkpointing technique integrated with MCU-EENet to manage and utilize harvested energy efficiently. Through extensive experimentation, our framework demonstrates significant energy savings and performance enhancements, making it a promising solution for energy-constrained environments. Experimental results show a ~ 30% to 50% reduction in energy footprint and a 1.2X to 2.4X speed improvement over existing checkpointing methods.
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Information
Published In
August 2024
384 pages
ISBN:9798400706882
DOI:10.1145/3665314
- Chair:
- Pascal Meinerzhagen,
- Program Chair:
- Kapil Dev,
- Program Co-chair:
- Jerald Yoo
This work is licensed under a Creative Commons Attribution International 4.0 License.
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- SIGDA: ACM Special Interest Group on Design Automation
- IEEE CAS
- IEEE EDA
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Association for Computing Machinery
New York, NY, United States
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Published: 09 September 2024
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ISLPED '24
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ISLPED '24: 29th ACM/IEEE International Symposium on Low Power Electronics and Design
August 5 - 7, 2024
CA, Newport Beach, USA
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