A Statistical Traffic Model for On-Chip Interconnection Networks

Network traffic modeling is a critical first step towards understanding and unraveling network power/performancerelated issues. Extensive prior research in the area of classic networks such as the Internet, Ethernet, and wireless LANs transporting TCP/IP, HTTP, and FTP traffic among others, has demonstrated how traffic models and model-based synthetic traffic generators can facilitate understanding of traffic characteristics and drive early-stage simulation to explore a large network design space. Though on-chip networks (a.k.a networks-on-chip (NoCs)) are becoming the de-facto scalable communication fabric in many-core systems-on-a-chip (SoCs) and chip multiprocessors (CMPs), no on-chip network traffic model that captures both spatial and temporal variations of traffic has been demonstrated yet. As available on-chip resources increase with technology scaling, enabling a myriad of new network architectures, NoCs need to be designed from the application's perspective. In this paper we propose such an empirically-derived network on-chip traffic model for homogeneous NoCs. Our comprehensive model is based on three statistical parameters described with a 3-tuple, and captures the spatio-temporal characteristics of NoC traffic accurately with less than 5% error when compared to actual NoC application traces gathered from fullsystem simulations of three different chip platforms. We illustrate two potential uses of our traffic model: how it allows us to characterize and gain insights on NoC traffic patterns, and how it can be used to generate synthetic traffic traces that can drive NoC design-space exploration.