This paper describes a few fundamental challenges concerning the design of Network-on-Chip (NoC) based multicores as the backbone of cyber-physical systems (CPS) for personalized medicine. One fundamental challenge in designing such CPS architectures is the need for a unifying mathematical description of the dynamical interactions between bio-physiological processes and cyber states. Another fundamental challenge is to build a rigorous mathematical optimization framework that allows the CPS to adapt to varying workloads and demands. To enable large-scale parallelism, we need a rigorous understanding of the CPS workloads that can guide the design and optimization of wired and wireless NoCs. We advocate for the development of goal-oriented self-organization algorithms that seek to both optimize specific design cost functions and maximize information about future system state. It is necessary to identify basic local rules of interaction not only for solving large scale optimization problems in a distributed fashion, but also for inducing an overall degree of autonomy and intelligence in the CPS architecture.
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Paul Bogdan (2015) studied this question.