Green AI Deployment on Resource-Constrained Devices: Software Engineering Approaches for Edge Inference and Intermittent Computing
Master Project Description
With the proliferation of AI-driven applications, there is a growing demand to move AI to the edge, deploying models on resource-constrained devices. However, deploying energy-efficient AI on such devices remains a challenge.
Research Topic Focus
- Review the current challenges and strategies in deploying AI models on resource-constrained devices.
- Investigate intermittent computing methodologies to optimize power and computational efficiency during AI model deployment.
- Explore and implement software engineering practices tailored for Raspberry PI 5 and SEEED studio devices.
- Optimize edge inference, assessing its compatibility and efficiency on the chosen platforms.
- Conduct a series of experiments to evaluate the performance, power consumption, and efficiency of deployed AI models.
- A curated set of best practices for deploying AI on resource-constrained devices with optimized energy consumption.
- Successful implementation and benchmarking of AI models on Raspberry PI 5 and SEEED studio using possibly intermittent computing.
- Demonstrable improvements in terms of energy efficiency and computational cost compared to traditional deployment methodologies.
- Acquire in-depth knowledge about the intricacies of deploying AI on resource-constrained devices.
- Gain hands-on experience with intermittent computing and edge inference techniques.
- Develop expertise in optimizing software engineering practices for Green AI applications.
- Understand the challenges and benefits of implementing Green AI on devices like Raspberry PI 5 and SEEED studio.
- Strong foundation in AI, machine learning, and software engineering principles.
- Proficiency in relevant programming languages and familiarity with edge devices like Raspberry PI 5 and SEEED studio.
- An interest in sustainable and energy-efficient technologies.
- Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2019). Green AI. arXiv preprint arXiv:1907.10597.
- Gu, L., Li, X., & Han, J. (2017). Sleepy pi: A ultra-low power energy harvesting platform for intermittent computing systems. In Proceedings of the 8th International Workshop on Real-World Embedded Wireless Systems and Networks (pp. 25-30).
Sagar Sen ( Arda Goknil (), Erik Johannes Husom ()