CNS Core: Small: Towards Timing-Predictable Autonomy in DNN-driven Embedded Systems

CNS 核心：小型：在 DNN 驱动的嵌入式系统中实现时序可预测的自主性

• 批准号: 2300525
• 负责人: Cong Liu
• 金额: $ 48.4万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300525&HistoricalAwards=false
• 关键词: CNS; Core; Small; Towards; Timing

Machine learning techniques, particularly deep neural networks (DNNs), are enabling dramatically better autonomy in important domains, such as robotics and transportation. For instance, in automotive systems, DNNs are used to map raw pixels from on-vehicle cameras to steering control decisions. Recent end-to-end self-driving frameworks even make it possible for DNNs to learn to self-steer from limited human driving datasets. NVIDIA and Audi recently announced their plans to deliver DNN-based automated vehicles. A major challenge of safely and reliably adopting DNNs in any safety-critical embedded systems (e.g., cars) is the need to ensure timing predictability (i.e., enabling timing constraints to be analytically validated at design time), which is one of the most important tenets in the certification required for such safety-critical systems. For example, the functional correctness of an automobile hinges crucially upon temporal correctness, as the control operations depend on the processing of certain environmental sensing and computation tasks within specific time constraints. Unfortunately, it is not straightforward to achieve timing predictability in such systems, due to the resource bottlenecks that DNNs can impose. The goal of this research is to achieve timing predictability in DNN-driven autonomous embedded systems. A novel system model leveraging Heijunka, a mature production leveling approach first developed by Toyota, will be established. New DNN-aware, real-time resource allocation methods and associated analysis techniques for validating timing constraints will be developed that can be applied in DNN-driven embedded systems. Moreover, an open-source ecosystem with efficient memory management under heterogeneous hardware architectures will be implemented. The outcome of this project will pave the way to enable DNN-driven solutions to be safely and confidently adopted in many embedded domains in which timing predictability is a natural requirement. This project will also result in a pipeline of computer engineers and scientists who are skilled in the interdisciplinary nature of DNN-driven embedded systems, as well as increase awareness of real-time and autonomous system design concepts among students at all academic levels.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.

机器学习技术，尤其是深度神经网络（DNNS），在重要领域（例如机器人技术和运输）中可以极大地更好地自治。例如，在汽车系统中，DNN用于将原始像素从车载摄像机映射到转向控制决策。最近的端到端自动驾驶框架甚至使DNN可以从有限的人类驾驶数据集中学习自动发展。 NVIDIA和奥迪最近宣布了他们提供基于DNN的自动车辆的计划。在任何安全关键的嵌入式系统（例如，汽车）中安全可靠地采用DNN的主要挑战是确保正时可预测性（即，在设计时间进行分析验证的时序限制），这是对此类安全临界系统所需的确认性原则之一。例如，汽车的功能正确性以时间正确性至关重要，因为控制操作取决于在特定时间限制内处理某些环境感应和计算任务的处理。不幸的是，由于DNN可以施加的资源瓶颈，在此类系统中实现定时可预测性并不直接。这项研究的目的是在DNN驱动的自动嵌入式系统中实现定时可预测性。将建立一个新型的系统模型，该模型将建立由丰田最初开发的成熟生产级别方法。将开发可在DNN驱动的嵌入式系统中应用的新的DNN感知，实时资源分配方法和相关的分析技术，以验证时间限制。此外，将实施一个在异质硬件体系结构下具有有效内存管理的开源生态系统。该项目的结果将为使DNN驱动的解决方案的安全和自信地在许多嵌入式域中被安排和自信地采用，在这些嵌入式域中，定时可预测性是自然要求。该项目还将导致计算机工程师和科学家的渠道，他们熟练于DNN驱动的嵌入式系统的跨学科性质，并提高对所有学术层面学生中实时和自主系统设计概念的认识。这项奖项在所有学术级别上。这项奖项反映了NSF的法定任务，并通过评估该基金会的智力效果，并通过评估了基金会的范围和广泛的范围。