Collaborative Research: DESC: Type I: FLEX: Building Future-proof Learning-Enabled Cyber-Physical Systems with Cross-Layer Extensible and Adaptive Design

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When an electronic device does not meet system requirements, it could become a waste of electrical and electronic equipment (WEEE). About 50 million metric tonnes of WEEE are generated annually worldwide, and it is estimated to increase to 111 million tonnes per year by 2050, causing immense environmental, human health, and socioeconomic damage. Retrofitting and reusing these devices may prolong their lifespan and reduce such negative impacts. For instance, in the case of a smartphone, extending its usage by just one year can cut its CO2 impact on the environment by 31%. This project enhances the retrofitting capability for emerging learning-enabled cyber-physical systems (LE-CPSs) to accommodate changes/updates with existing hardware, so as to increase device lifespan, reduce e-waste and improve sustainability. The success of this project could foster a more sustainable future, with far-reaching impacts spanning across environmental, economic, and societal dimensions. By enhancing retrofitting capabilities, the lifespan of electronic devices can be effectively extended, consequently mitigating the necessity for new products and reducing the cumulative quantity of electronic waste generated. Improved retrofitting can also lead to more energy-efficient systems, reducing greenhouse gas emissions and mitigating climate change. Moreover, retrofitting electronic systems can lead to cost savings for both businesses and consumers, increasing access to affordable technology, especially for economically disadvantaged communities. The project catalyzes research in several communities: design automation, cyber-physical systems, machine learning, and domain experts in multi-agent system applications. This project also generates broader impacts through curriculum development, broadening participation in computing, K-12 outreach activities, and international design contests.Retrofitting could be quite challenging, especially for emerging LE-CPSs such as autonomous vehicles, medical devices, and robots, which often operate within a constantly-changing physical environment, have limited resources and stringent timing requirements, and employ complex and resource-consuming machine learning techniques. To bridge the gap between functionality and architecture during retrofitting and prolong system lifetime, this project develops FLEX, a cross-layer framework that on the one hand makes the architecture of LE-CPSs more extensible to facilitate accommodating changes with existing hardware, and on the other hand makes their functionality more adaptive with respect to resource limitations and environment changes. The framework includes novel (1) system-level extensibility-driven design and retrofitting methods that at the design time explore the design space and trades off future extensibility of LE-CPSs with other system objectives and at the retrofitting time leverage the robustness of existing functionality in LE-CPSs to further expand scheduling slack and accommodate retrofitting needs based on a weakly-hard paradigm; (2) resource adaptability-driven neural architecture model and design methods that provide multiple designs of neural networks (e.g., with multiple exists) to enable resource-aware configuration during retrofitting; and (3) continual and on-device learning methods that enable LE-CPSs to effectively adapt to the changing physical environment and system input with little supervision for increasing system lifetime.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.