Speculative path power estimation using trace-driven simulations during high-level design phase

Today power is an important design metric and the ongoing goal of microprocessor designers is to maximize performance within specified power targets. The key to achieving this goal is the ability to accurately estimate power and performance design points of future products during the high-level micro-architectural design phase (HLD). These estimates are heavily used for feature analysis and product feasibility studies. Most performance and power simulators across the industry use the trace-driven simulation model (TDM) as opposed to an execution driven model (EDM). This is because, in general, trace-driven models: (i) have faster turnaround time; (ii) require significantly lower resources in terms of disk space, CPU time and memory footprint; and (iii) are more robust, portable and well understood. However, TDM simulations lack the ability to accurately capture the flow of speculative path (or wrong path) 1 execution following a branch mispredict in an out-of-order processor pipeline. This leads to inaccuracies in power and performance estimates. On the other hand, in the EDM method, input is an executable and the model can fetch and execute instructions down the speculative path on a branch mispredict. As such it enables us to accurately account for the impact of the speculative path activity. However, it is slower, prone to failures, and has much higher development and validation effort. In this paper we compare and analyze performance and power estimates from TDM and EDM simulations for the same workload phases. We observe that the impact of wrong path on power estimates is significantly higher than on the performance estimates. Using results from our analysis, we develop a methodology to account for power consumption during wrong path execution in TDM simulations. We show that the proposed methodology can provide power estimates approaching EDM-based accuracy while not sacrificing the speed and flexibility of the trace-driven models.