Decoding the Laws of Cyber-Physical Systems Design
Cyber-physical systems (CPS) interweave computation, communication, and control to facilitate our interaction with the physical world. Within this paradigm, we will discuss our efforts on designing autonomous computing systems, deriving the governing equations from complex data, and reconstructing interdependencies and mining their geometry. Towards this end, we define new theoretical foundations and master the spatio-temporal complexity of CPS for modeling, analyzing, and optimizing their operations. We propose a new mathematical strategy for constructing compact yet accurate models of CPS that can capture their non-linear, non-Gaussian, and/or fractal structure through a minimum number of parameters while preserving a high degree of modeling fidelity and prediction accuracy. The benefits of this mathematical modeling are tested in the context of a CPS approach to brain-machine interface for decoding human intent. Making the CPS paradigm a reality requires not only modeling and control algorithms, but also autonomous streaming and steering computing systems. To tackle the gap between the programming flexibility of general-purpose processors and the efficiency of specialized processors, we set forth a design methodology for self-programmable and self-optimizing computing architectures.