Network-on-Chip-Enabled Multicore Platforms for Parallel Model Predictive Control

Internet-of-Things architecture aims to provide smart connectivity not only with existing computers, but also with new context-aware computing resources, extending soon beyond von Neumann devices for the purpose of mining, prediction, and control of cyber and physical components. These cyber-physical systems (CPSs) not only lead to the accumulation of large amounts of data that can be used to build comprehensive mathematical models, but also raise the quest for real-time analysis and control in diverse application domains, such as environment, healthcare, avionics, smart interconnected automobiles, and smart buildings. Endowing the CPS with a higher degree of distributed smartness and cognition (adaptation) to process massive amounts of data requires efficient control modules. In addition, the prohibitive nature of power consumption, data movement, and memory bandwidth issues calls for a shift of processing the decision-making strategies from within large supercomputing centers closer to the actual sensing site via many distributed networks-on-chip (NoCs)-based multicore platforms. Toward this end, in this paper, we propose an efficient NoC-based multicore architecture capable of solving large-scale nonlinear model predictive control (NMPC) problems. By carefully analyzing the spatiotemporal workload characteristics of the NMPC problems, we propose the design of an efficient NoC architecture. Our proposed NoC architecture achieves up to 29% improvement in latency and 28% improvement in energy dissipation over the conventional mesh NoC-based counterpart.