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

该项目开发基础半导体和协同设计方法，用于边缘的风险感知推理。用于深度学习的风险感知推理程序，例如深度神经网络（DNN）的贝叶斯推理，可以提取预测和预测风险，但也需要大量的计算。推理过程操作在随机数和统计密度函数上，而不是真实的数，因为它们具有更高的表达性。与此同时，他们独特的工作负载为传统半导体和专为数字工作负载设计的微电子带来了关键的复杂性。为了应对不同的处理挑战，该团队资助提案探索了基于二维（2D）材料和协同设计方法的新型设备的概念，这些设备可以使用超低功耗非冯·诺依曼执行来处理主要的推理操作。将开发一个跨层仿真工具，以积极地将材料和设备级的新颖性与计算模型和架构设计空间联系起来，并探索非常规的系统设计概念。特别是我们的各种合作设计计划将被用于探索昆虫规模无人机的自主导航作为测试平台。自主昆虫规模的无人机可以产生前所未有的应用，例如，我们的项目有可能将一个典型的物联网变成一个飞行的物联网，其中昆虫规模的无人机上的传感器可以在太空中不断自我组织，以应对不断变化的环境条件。深度神经网络（DNN）的数据驱动学习显著简化了许多决策问题模型抽象的复杂性。然而，生成的DNN就像一个黑盒子，不能为预测的准确性提供理论保证。值得注意的是，随着我们对基于DNN的预测模型在使命和安全关键型应用中的依赖越来越大，有必要评估DNN的预测何时可能不准确。这个团队资助项目探索了未来半导体技术的概念，在这些技术中，可以在边缘设备的时间和能量范围内操作时提取更具表现力的DNN推理，其中包括预测和预测风险。此外，我们还将举办新兴协同设计方法研讨会，以满足未来半导体技术和设计对劳动力和人才的需求。我们将通过开发跨学科课程讲座和高级设计项目，在本科生和研究生中灌输共同设计技能。我们还将通过创建一个在线资源中心（如开源设计工具包、设备模型和协同设计探索工具）来培养一个共同设计空间探索的研究人员社区。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.