Burton-Barr, J., Fernando, B., & Rajan, D. (2024). Activation Control of Vision Models for Sustainable AI Systems. IEEE Transactions on Artificial Intelligence, 5(7), 3470–3481. https://doi.org/10.1109/tai.2024.3372935
Abstract:
As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology.
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Funding Info:
This research is supported by core funding from: Science and Engineering Research Council, Central Research Fund
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This research / project is supported by the Agency for Science, Technology, and Research (A*STAR) - Centre for Frontier AI Research (CFAR)
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