UBC Theses and Dissertations

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UBC Theses and Dissertations

EdgeEngine : a thermal-aware optimization framework for edge inference Ahmadi, Amirhossein


Heterogeneous edge platforms enable the efficient execution of machine learning inference applications. These applications often have a critical constraint (such as meeting a deadline) and an optimization goal (such as minimizing energy consumption). To navigate this space, existing optimization frameworks adjust the platform's frequency configuration for the CPU, the GPU and/or the memory controller. These optimization frameworks, however, are thermal-oblivious disregarding the fact that edge platforms are frequently deployed in environments where they are exposed to ambient temperature variations. In this thesis, we first characterize the impact of ambient temperature on the power consumption and execution time of machine learning inference applications running on a popular edge platform, the NVIDIA Jetson TX2. Our rigorous data collection and statistical methodology reveals a sizeable ambient temperature impact on power consumption (about 20\% on average, and up to 40\% on some workloads) and a moderate impact on runtime (up to 5\%). We also find that existing, thermal-oblivious optimization frameworks select frequency configurations that either violate the application's constraints and/or are sub-optimal in terms of the optimization goal assigned. To address these shortcomings, we propose EdgeEngine, a lightweight thermal-aware optimization framework. EdgeEngine monitors the platform's temperature and uses reinforcement learning to adjust the frequency configuration of all underlying platform resources to meet the application's constraints. We find that EdgeEngine meets the application's constraint, and achieves up to 29\% lower energy consumption (up to 2x) and up to 41\% fewer violations compared to existing state-of-the-art thermal-oblivious optimization frameworks.

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