NoCEE : Energy macro-model extraction methodology for network on chip routers

In this paper we present NoCEE, a fast and accurate method for extracting energy models for packet-switched Network on Chip (NoC) routers. Linear regression is used to model the relationship between events occurring in the NoC and energy consumption. The resulting models are cycle accurate and can be applied to different technology libraries. We verify the individual router estimation models with many different synthetically generated traffic patterns and data inputs. Characterization of a small library takes about two hours. The mean absolute energy estimation error of the resultant models is 5% (10% max) against a complete gate level simulation. We also apply this method to a number of complete NoCs with inputs extracted from synthetic application traces and compare our estimated results to the gate level power simulations (mean absolute error is 5%). Our estimation methodology has been integrated with commercial logic synthesis flow and power estimation tools (Synopsys Design Compiler and PrimePower), allowing application across different designs. The extracted models show the different trends across various parameterizations of Network on Chip routers and have been integrated into an architecture exploration framework.