A multi-objective genetic approach to mapping problem on Network-on-Chip
Advances in technology now make it possible to integrate hundreds of cores (e.g. general or special purpose processors, embedded memories, application specific components, mixed-signal I/O cores) in a single silicon die. The large number of resources that have to communicate makes the use of interconnection systems based on shared buses inefficient. One way to solve the problem of on-chip communications is to use a Network-on-Chip (NoC)-based communication infrastructure. Such interconnection systems offer new degrees of freedom, exploration of which may reveal significant optimization possibilities: the possibility of arranging the computing and storage resources in an NoC, for example, has a great impact on various performance indexes. The paper addresses the problem of topological mapping of intellectual properties (IPs) on the tiles of a mesh-based NoC architecture. The aim is to obtain the Pareto mappings that maximize performance and minimize power dissipation. We propose a heuristic technique based on evolutionary computing to obtain an optimal approximation of the Pareto-optimal front in an efficient and accurate way. At the same time, two of the most widely-known approaches to mapping in mesh-based NoC architectures are extended in order to explore the mapping space in a multi-criteria mode. The approaches are then evaluated and compared, in terms of both accuracy and efficiency, on a platform based on an event-driven trace-based simulator which makes it possible to take account of important dynamic effects that have a great impact on mapping. The evaluation performed on both synthesized traffic and real applications (an MPEG-4 codec) confirms the efficiency, accuracy and scalability of the proposed approach.
mapping network-on-chip simulation system-on-chip multi-objective optimization evolutionary algorithms