Explicit Modeling of Control and Data for Improved NoC Router Estimation
Networks-on-Chip (NoCs) are scalable fabrics for interconnection networks used in many-core architectures. ORION2.0 is a widely adopted NoC power and area estimation tool; however, its models for area, power and gate count can have large errors (up to 110% on average) versus actual implementation. In this paper, the authors propose a new methodology that analyzes netlists of NoC routers that have been placed and routed by commercial tools, and then performs explicit modeling of control and data paths followed by regression analysis to create highly accurate gate count, area and power models for NoCs. When compared with actual implementations, their new models have average estimation errors of no more than 9:8% across micro-architecture and implementation parameters.