Collaborative Research: SHF: Small: An Accelerated Computation Architecture for State Transition Representation and Applications

Real-world applications, such as software modeling, digital circuit design, manufacturing control, and status modeling of smart devices and smart systems, often require efficient techniques to model their behaviors and changes over time. Based on their specific requirements, different algorithms (including machine learning) are needed, such as reachability computation, pathfinding, and state prediction. For example, the graph neural network (GNN) algorithm can help to learn the compact vector representations of the states and transitions to capture the complex patterns and dependencies. However, existing computation architectures for such techniques are not very efficient for two major reasons: (i) the algorithms are not computationally efficient, and (ii) the data size is very large. This research pioneers the development of an accelerated computation architecture for system modeling techniques and applying them to critical smart environment applications. This project will address the growing national need for professionals in accelerated computation architecture, algorithms, and machine learning. The research will produce an accelerated computation architecture that serves as a foundational tool for fellow science and engineering practitioners in academia and industry. Educational initiatives integrate the research findings into graduate and undergraduate curriculum development. Additionally, outreach and educational activities are conducted to promote participation from K-12 and undergraduate students from populations underrepresented in computing. The overarching goal of this project is to design an accelerated computation architecture for state modeling techniques and to apply them to important smart environment applications. Towards that, this project includes three synergistic research thrusts. Specifically, Thrust 1 designs efficient computation techniques to accelerate the reachability computation in a state transition representation, which can be used to detect if any undesired (e.g., unsafe) state is reachable. Thrust 2 accelerates the computation of graph machine learning algorithms by adaptively reducing the overhead of instant updates and maintaining high-quality communities. Thrust 3 applies the techniques in Thrusts 1 and 2 to an important application domain of smart environments. 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.