Toward Efficient Design Space Exploration for Fault-Tolerant Multiprocessor Systems

The design space exploration (DSE) of fault-tolerant multiprocessor systems is very complex, as it contains three interacting NP-hard problems: 1) task hardening; 2) task mapping; and 3) task scheduling. In addition, replication-based task hardening can introduce new tasks, called replicas, into the system, enlarging the design space further. As a population-based global optimization algorithm, evolutionary algorithms (EAs) have been widely used to explore this huge design space over the last decade. However, as analyzed in this paper, the search space of previous works is highly redundant, resulting in poor efficiency and scalability. This paper proposes an efficient EA-based DSE method for the design of large-scale fault-tolerant multiprocessor systems. The main novelties of this paper include: 1) mapping exploration is explicitly separated, i.e., task mapping is optimized during the evolutionary search, while replica mapping is constructed heuristically according to the current co-synthesis state; 2) the design space of task hardening and task mapping are explored independently by a cooperative co-EA; and 3) as a complement to global search of EA, problem-specific local search operators are designed for both task hardening and task mapping, reducing the number of fitness evaluations required. Compared with the most relevant state-of-the-art method, the superiority of the proposed method is demonstrated using extensive experiments on a large set of benchmarks, e.g., 1.75\times \sim 2.50\times better results can be obtained on the benchmarks of 300 tasks and 30 processors. © 1997-2012 IEEE.