Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Bio-inspired self-repairing hardware is a distributed self-adaptive system, characterized by powerful fault-tolerant ability and environment adaptivity. However, it suffers from some difficulties such as large resource consumption and degraded circuit performances. From the viewpoint of cybernetics and computer science, the cellular differentiation and substitution process of bio-inspired self-repairing hardware can be converted into dynamic placement problems on a reconfigurable system. Current systems can only generate some predefined fault-free placements from a finite number of initial placements. The aim of this paper was to achieve high-quality placements from arbitrary initial placements. Based on P systems, an analysis has been made on the limitations of current systems and an improved computing system has been developed to achieve the ergodic property. Its computational power has been verified by a constructive proof. Moreover, centering on the problem how to improve the placement quality on a distributed platform, the optimization model, task allocation, optimization strategy, and membrane optimization algorithm have been designed and developed. The optimization performances were verified and the calculation amount was exhibited by experiments. Finally, it indicated by comparison that the proposed approach would reduce the resource consumption and maintain good circuit performances.