CNS Core: Small: Enabling Real-time, Scalable and Secure Collaborative Intelligence on the Edge

With the proliferation of embedded systems, multicore computing devices enable the recent trend of moving computation from the centralized cloud to distributed edge platforms. This trend yields new products and services across smart infrastructures in smart cities. However, as real-time workloads are executed at the edge computing platforms, the performance bottleneck is transferred from the edge-cloud communication to on-chip communication.

The system’s real-time performance faces new system-architectural challenges for the Network-On-Chip (NoC), which are scalability and security. These challenges are hinged with dynamic data distributions across different users. This project aims to design a real-time and scalable NoC for implementing real-time collaborative learning algorithms.

The key strategy is to orchestrate a system-architecture and algorithm co-design to explore the new design space on the edge computing platform.

To cope with the research challenges, a comprehensive architecture will be developed to address these multifaceted problems through a hardware and software co-design, which consists of three key thrusts: (i) designing an interconnect, which will eliminate non-predictability barrier on the NoC; (ii) establishing a scalable virtualized transaction environment for the collaborative learning system to guarantee that all the real-time transaction tasks can complete at the right time; (iii) implementing a real-time and secure multi-target tracking system on the edge platform in light of the newly proposed architecture. The proposed research will be evaluated using the physical platform Equinox, with indoor and outdoor studies beyond simulation.

This research will open a new dimension of research and educational opportunities. In particular, the success of the project will provide a hardware/software package that can enhance the real-time collaborative computing on the edge. The resulted interconnect and Equinox are ready-to-use platforms that will allow experts/researchers to easily examine their research designs regarding collaborative learning and real-time edge computing, thereby sealing the gap between different research fields.

Educational efforts will be devoted to (i) curriculum design for the undergraduate and graduate program, (ii) summer camp development for middle and high school students, and teachers, (iii) broadening participation in computing and engineering, at the Wayne State University.

The data, codes, simulators, and platforms developed in this project will be made available publicly throughout the duration of the project and for at least five years after the end of the project. The project repository will be available on Wayne State University website (http://zheng.eng.wayne.edu/index.html) and the website of the CAR Lab (https://www.thecarlab.org/.

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.