Statues: Energy-Efficient Video Object Detection on Edge Security Devices with Computational Skipping
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Abstract
This paper proposes a software and hardware co-optimization method tailored to object detection on edge security devices. Object detection on video inputs requires a massive number of MAC computations, so it is difficult to implement a real-time inference task with limited computational budget on edge security devices. To relieve such computational complexity, we propose a Statues, selective computational skipping approach by scoring pixel differences in the recent inputs. If the score is low enough, we skip the rest DNN part because we expect the almost same object detection result as the previous frame. Our Statues approach maximizes energy-efficiency by 44% compared with the conventional method with negligible accuracy drop.
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Index Terms
- Statues: Energy-Efficient Video Object Detection on Edge Security Devices with Computational Skipping
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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-NonCommercial-ShareAlike 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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