CCF: Small: Real-Number Function Encoding Driven Error Resilient Signal Processing and Control: Application to Nonlinear Systems from Adaptive Filters to DNNs

CCF：小型：实数函数编码驱动的误差弹性信号处理和控制：从自适应滤波器到 DNN 的非线性系统应用

• 批准号: 2128419
• 负责人: Abhijit Chatterjee
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128419&HistoricalAwards=false
• 关键词: CCF; Small; Real; Number; Function; CCF; Small; Real; Number; Function

Technology scaling, device integration and high circuit speeds have increased the risk of errors in digital processors running computing and control applications. Such errors can jeopardize the operational safety of autonomous systems employing embedded processors in the field, causing severe loss of performance, accidents, or damage to life and property. To put this in perspective, applications such as self-driving cars and drones demand tera-ops of computation throughput and yet must perform with exacting levels of reliability and safety. The latter reliability and safety demands must be met with minimal degrees of hardware and software redundancy without negatively impacting dependability, power consumption, payload, form factor and cost considerations that are critical for commercial success. To alleviate relevant safety threats, this project aims to deploy low-cost and efficient methods for detecting and mitigating the effects of monitored errors in real time and in the field as effectively and rapidly as possible. This will enable a paradigm shift in the way failure-tolerance technologies are applied to autonomous systems while maintaining their reliability, affordability and cost. The project integrates research with education involving development of educational infrastructure for teaching and laboratory work, incorporation of diversity in student participation, exchanges with industry, technology transfer and engagement with undergraduate and high-school students, all with the goal of producing highly qualified trained engineers for the workplace of the future.Today, implementing failure tolerance for nonlinear signal-processing and control algorithms is dependent on some form of computation duplication. The algorithm-based fault-tolerance techniques of the past were designed mostly for linear computations and are not directly applicable to error control in nonlinear computations of modern autonomous systems. To resolve this, the project is built around the concept of algorithmic checks that encode nonlinear computations with linearized or nonlinear checking mechanisms. The checks are created using analytical methods for weakly nonlinear systems or by machine-learning algorithms for generic nonlinear systems and enable real-time error detection in switched capacitor circuits, nonlinear digital filters, adaptive state-estimation filters, nonlinear control algorithms and deep neural networks. Error correction is performed via state restoration in which the system state after error detection is restored to a prior fault-free system state or by using probabilistic error-compensation techniques. The core techniques can be applied to different aspects of the core computations performed in embedded computing infrastructure for autonomous systems, namely perception, intelligence, decision-making and control to make them error-resilient and trustworthy. The underlying science is enabling the design of entirely new classes of self-checking reliable autonomous systems that are beyond the scope of the current state of the art.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.

技术缩放，设备集成和高电路速度增加了运行计算和控制应用程序的数字处理器错误的风险。 这样的错误可能会危害在现场使用嵌入式处理器的自主系统的运营安全，从而导致绩效，事故或生命和财产损失严重丧失。简而言之，自动驾驶汽车和无人机之类的应用需要计算吞吐量的tera-op-op-op，但必须以严格的可靠性和安全性来执行。后者的可靠性和安全要求必须以最低程度的硬件和软件冗余程度来满足，而不会对可靠性，功耗，有效负载，表格和成本考虑因素产生负面影响，这对于商业成功至关重要。为了减轻相关的安全威胁，该项目旨在部署低成本和有效的方法，以尽可能有效，迅速地在实时和现场中检测和减轻监视错误的影响。这将使能够在自主系统上应用于自主系统的方式进行范式转变，同时保持其可靠性，负担能力和成本。 该项目将研究与涉及教育基础架构开发进行教学和实验室工作的教育进行整合，将多样性纳入学生参与，与工业，技术转移和与本科和高中生的交流交流，所有这些目标都旨在为未来的工作场所提供高素质的训练有素的工程师，以实现某些计算机，并控制某些依赖性的信号代理，以实现非专业的依赖性。重复。过去的基于算法的故障耐受性技术主要是为线性计算而设计的，并不直接适用于现代自治系统非线性计算中的误差控制。为了解决这一问题，该项目围绕着算法检查的概念，该算法检查用线性化或非线性检查机制编码非线性计算。 使用用于弱非线性系统的分析方法或通过用于通用非线性系统的机器学习算法创建检查，并在开关电容器电路，非线性数字过滤器，自适应状态估算过滤器，非线性控制算法和深神经网络中实现实时错误检测。误差校正是通过状态恢复进行的，在该状态恢复中，将错误检测后的系统状态恢复为先前的无故障系统状态或使用概率错误补偿技术。 核心技术可以应用于在自主系统的嵌入式计算基础架构中执行的核心计算的不同方面，即感知，智能，决策和控制，以使其使其错误弹性和可信赖。潜在的科学使全新的自我检查可靠自主系统的设计超出了现有艺术状态的范围。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来进行评估的。