NATIONAL SCIENCE FOUNDATION
Beginning Fiscal Year:
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.