FuSe-TG: Ultra-low-power and Robust Autonomy of Edge Robotics with 2D Semiconductors

This project develops foundational semiconductors and co-design methodologies for risk-aware inference at the edge. Risk-aware inference procedures for deep learning such as Bayesian inference of deep neural networks (DNNs) can extract both prediction and prediction risks but also demand overwhelming computations. The inference procedures operate on random numbers and statistical density functions instead of real numbers for their higher expressivity. Meanwhile, their unique workload presents critical complexities for traditional semiconductors and microelectronics designed for digital workloads. To meet the distinct processing challenges, this teaming grant proposal explores the concepts of novel devices based on two-dimensional (2D) materials and co-design methodologies that can process predominant inference operations using an ultra-low-power non-von Neumann execution. A cross-layer simulation tool will be developed to actively bridge the material and device-level novelties to computing model and architecture design space and to explore unconventional system design concepts. Especially our various co-design initiatives will be intersected into exploring autonomous navigation of insect-scale drones as a test platform. Autonomous insect-scale drones can engender unprecedented applications, e.g., our project can potentially enliven a typical internet-of-things into a flying internet-of-things where the sensors riding on insect-scale drones can continually self-organize in space against the changing environmental conditions. The data-driven learning of deep neural networks (DNNs) significantly simplifies the complexity of model abstraction for many decision-making problems. Yet, the generated DNNs act like a black box and do not offer theoretical guarantees on the accuracy of the predictions. Significantly as our reliance on DNN-based predictive models is increasing for mission and safety-critical applications, it has become necessary to assess when DNN’s predictions are likely inaccurate. This teaming grant project explores concepts for future semiconductor technologies where more expressive DNN inference, where both the prediction and prediction risks, can be extracted while operating within the time and energy bounds of edge devices. Additionally, we will develop workshops on emerging co-design methodologies to address the workforce and talent demand for future semiconductor technologies and designs. We will instill co-design skills among undergraduate and graduate students by developing interdisciplinary course lectures and senior design projects. We will also foster a community of researchers on co-design space exploration by creating an online hub of resources such as open-source design kits, device models, and co-design exploration tools.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.

This project develops foundational semiconductors and co-design methodologies for risk-aware inference at the edge. Risk-aware inference procedures for deep learning such as Bayesian inference of deep neural networks (DNNs) can extract both prediction and prediction risks but also demand overwhelming computations. The inference procedures operate on random numbers and statistical density functions instead of real numbers for their higher expressivity. Meanwhile, their unique workload presents critical complexities for traditional semiconductors and microelectronics designed for digital workloads. To meet the distinct processing challenges, this teaming grant proposal explores the concepts of novel devices based on two-dimensional (2D) materials and co-design methodologies that can process predominant inference operations using an ultra-low-power non-von Neumann execution. A cross-layer simulation tool will be developed to actively bridge the material and device-level novelties to computing model and architecture design space and to explore unconventional system design concepts. Especially our various co-design initiatives will be intersected into exploring autonomous navigation of insect-scale drones as a test platform. Autonomous insect-scale drones can engender unprecedented applications, e.g., our project can potentially enliven a typical internet-of-things into a flying internet-of-things where the sensors riding on insect-scale drones can continually self-organize in space against the changing environmental conditions. The data-driven learning of deep neural networks (DNNs) significantly simplifies the complexity of model abstraction for many decision-making problems. Yet, the generated DNNs act like a black box and do not offer theoretical guarantees on the accuracy of the predictions. Significantly as our reliance on DNN-based predictive models is increasing for mission and safety-critical applications, it has become necessary to assess when DNN’s predictions are likely inaccurate. This teaming grant project explores concepts for future semiconductor technologies where more expressive DNN inference, where both the prediction and prediction risks, can be extracted while operating within the time and energy bounds of edge devices. Additionally, we will develop workshops on emerging co-design methodologies to address the workforce and talent demand for future semiconductor technologies and designs. We will instill co-design skills among undergraduate and graduate students by developing interdisciplinary course lectures and senior design projects. We will also foster a community of researchers on co-design space exploration by creating an online hub of resources such as open-source design kits, device models, and co-design exploration tools.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.