Abstract
Context. The rise of use cases of AI catered towards the Edge, where devices have limited computation power and storage capabilities, motivates the need for better understating of how AI performs and consumes energy. Goal. The aim of this paper is to empirically assess the impact of three different AI containerization strategies on the energy consumption, execution time, CPU, and memory usage for computer-vision tasks on the Edge. Method. In this paper we conduct an experiment with the used containerization strategy as main factor, with three treatments: ONNX Runtime, WebAssembly, and Docker. The subjects of the experiment are four widely-used computer-vision algorithms. We then orchestrate a series of runs where we deploy the four subjects on different generations of Raspberry Pi devices, with different hardware capabilities. A total of 120 runs (per device) are recorded to gather data on energy, execution time, CPU, and memory. Results. We found a statistically significant difference between the three containerization strategies on all dependent variables. Specifically, WebAssembly proves to be a valuable alternative for devices with reduced disk space and computation power. Conclusions. For computer-vision tasks with limited disk space and RAM memory requirements, developers should prefer WebAssembly for deployment. The (non-dockerized) ONNX Runtime resulted to be the best choice in terms of energy consumption and execution time.
Original language | English |
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Title of host publication | EASE 2022 |
Subtitle of host publication | Proceedings of the ACM International Conference on Evaluation and Assessment in Software Engineering 2022 |
Publisher | Association for Computing Machinery |
Pages | 50-59 |
Number of pages | 10 |
ISBN (Electronic) | 9781450396134 |
DOIs | |
Publication status | Published - Jun 2022 |
Event | 26th ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022 - Gothenburg, Sweden Duration: 13 Jun 2022 → 15 Jun 2022 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 26th ACM International Conference on Evaluation and Assessment in Software Engineering, EASE 2022 |
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Country/Territory | Sweden |
City | Gothenburg |
Period | 13/06/22 → 15/06/22 |
Bibliographical note
Funding Information:As future work, we are planning to investigate on how energy correlates with (additional) performance-related metrics, replicate the experiment by using other AI-based tasks (e.g., conversational AI) and other configurations/hardware platforms. ACKNOWLEDGMENTS This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 871342.
Funding Information:
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 871342.
Publisher Copyright:
© 2022 Owner/Author.
Funding
As future work, we are planning to investigate on how energy correlates with (additional) performance-related metrics, replicate the experiment by using other AI-based tasks (e.g., conversational AI) and other configurations/hardware platforms. ACKNOWLEDGMENTS This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 871342. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 871342.