Collaborative Research: CPS: Medium: Real-time Criticality-Aware Neural Networks for Mission-critical Cyber-Physical Systems

人工智能（AI）的进步清楚地表明，智能系统将成为科学进步的下一个飞跃，以实现无数未来应用，提高生活质量，促进经济发展，并增强社会对各种干扰的适应力。然而，人工智能的进步需要付出相当大的资源成本。为了降低人工智能的成本，该项目从生物系统中汲取灵感。众所周知，人工智能的一个关键瓶颈是感知子系统。它是允许AI感知和理解其周围环境的部分。人类非常善于理解环境中的关键因素，人类的感知系统会自动将有限的认知资源集中在场景中最重要的元素上，从而节省了大量的“大脑处理能力”。目前的人工智能管道没有类似的机制，导致资源成本显着增加。该项目重构了数据分析和机器智能管道，以更好地优先考虑外部刺激，利用并显著扩展了实时系统研究社区先前开发的调度方面的进展。重构后的人工智能管道将提高人工智能系统的效率和功效，使其更加安全和响应速度更快，同时显着降低成本。如果成功，该项目将有助于将机器智能解决方案带给全社会。这是通过研究，教育和推广之间的互动以及多个科学界的整合来实现的，包括（i）提供平台和插件的嵌入式计算研究人员，（ii）物联网和网络研究人员，以及（iii）智能应用研究人员和应用领域专家。这项工作是网络物理计算研究的一个例子，新一代的数字算法学习利用对物理系统的更好理解来改善社会成果。 该项目从现代基于神经网络的网络物理应用中的机器智能管道中删除了系统优先级反转。一般来说，优先级反转发生在实时系统中，当计算不太关键（或具有较长的截止期限）时，优先级反转发生在那些更关键（或具有较短的截止期限）的计算之前。机器智能软件的当前状态在从感知到决策的路径上遭受显著的优先级反转，导致系统对关键事件的响应性大大降低，从而危及安全性并增加满足应用需求的硬件成本。通过解决这个问题，本项目将提高系统对关键输入的反应能力，同时显著降低平台成本。该项目的智力价值在于研究网络物理计算中两个核心领域的交叉点：（i）数据分析和机器学习;（ii）实时系统。具体来说，该项目重构了数据分析和机器智能管道，以消除优先级反转。缓解不同系统中的优先级反转问题一直是实时社区的关键贡献之一。从机器智能管道中删除优先级反转还做出了其他几项科学贡献。也就是说，（i）重构的人工智能管道提高了人工智能支持的关键任务系统的效率和功效，（ii）它使自主系统能够更快地响应，同时降低成本，（iii）它通过确保首先处理关键输入来促进智能系统的安全性。该项目预计将展示现代基于机器学习的推理协议性能的显着改进，同时提供服务差异化，大大提高对关键情况的反应的可预测性和及时性。如果成功，该项目将大大降低在未来网络物理系统中部署机器智能解决方案的成本，同时提高可预测性和时间保证。除了提供该项目的技术贡献外，这项工作的一个明确目的是促进智能CPS主题的教育和劳动力发展。这是通过研究，教育和扩大参与活动之间的互动以及多个社区的整合来实现的，包括（i）提供平台和嵌入式计算的研究人员，（ii）物联网和网络的研究人员，以及（iii）该奖项反映了NSF的法定使命，并被认为是值得支持的，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。

Advances in artificial intelligence (AI) make it clear that intelligent systems will account for the next leap in scientific progress to enable a myriad of future applications that improve the quality of life, contribute to the economy, and enhance societal resilience to a broad spectrum of disruptions. Yet, advances in AI come at a considerable resource costs. To reduce the cost of AI, this project takes inspiration from biological systems. It is well-known that a key bottleneck in AI is the perception subsystem. It is the part that allows AI to perceive and understand its surroundings. Humans are very good at understanding what’s critical in their environment and the human perceptual system automatically focuses limited cognitive resources on those elements of the scene that matter most, saving a significant amount of “brain processing power”. Current AI pipelines do not have a similar mechanism, resulting in significantly higher resource costs. The project refactors data analytics and machine intelligence pipelines to allow for better prioritization of external stimuli leveraging and significantly extending advances in scheduling previously developed in the real-time systems research community. The refactored AI pipeline will improve the efficiency and efficacy of AI-enabled systems, allowing them to be safer and more responsive, while at the same time significantly lowering their cost. If successful, the project will help bring machine intelligence solutions to the benefit of all society. This is achieved through interactions between research, education, and outreach, as well as integration of multiple scientific communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts. The work is an example of cyber-physical computing research, where a new generation of digital algorithms learn to exploit a better understanding of physical systems in order to improve societal outcomes. The project removes systemic priority inversion from machine intelligence pipelines in modern neural-network-based cyber-physical applications. In general, priority inversion occurs in real-time systems when computations that are less critical (or with longer deadlines) are performed ahead of those that are more critical (or with shorter deadlines). The current state of machine intelligence software suffers from significant priority inversion on the path from perception to decision-making, resulting in vastly inferior system responsiveness to critical events, thereby jeopardizing safety and increasing the cost of hardware to meet application needs. By resolving this problem, this project shall improve system ability to react to critical inputs, while at the same time significantly reducing platform cost. The intellectual merit of the project lies in investigating the intersection of two core areas in cyber-physical computing: (i) data analytics and machine learning and (ii) real-time systems. Specifically, the project refactors data analytics and machine intelligence pipelines to remove priority inversion. Mitigation of priority inversion problems in different systems has been one of the key contributions of the real-time community. Removal of priority inversion from machine intelligence pipelines makes several other scientific contributions. Namely, (i) the refactored AI pipeline improves the efficiency and efficacy of AI-enabled mission-critical systems, (ii) it enables autonomous systems to be more responsive, while lowering their cost, and (iii) it contributes to safety of intelligent systems by ensuring that critical inputs are processed first. The project expects to demonstrate significant improvements in performance of modern machine-learning-based inference protocols, while offering service differentiation that dramatically improves predictability and timeliness of reactions to critical situations. If successful, the project will significantly reduce the cost of deploying machine intelligence solutions in future cyber-physical systems, while improving predictability and temporal guarantees. In addition to delivering the technical contributions of this project, an explicit purpose of the work is to advance education and workforce development on Intelligent CPS topics. This is achieved through interactions between activities for research, education, and broadening participation, as well as integration of multiple communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts.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.