Automated design of application-specific superscalar processors

Automated design of superscalar processors can provide future system-on-chip (SOC) designers with a turn-key method of generating superscalar processors that are Pareto-optimal in terms of performance, energy consumption, and area for the target application program(s). Unfortunately, current optimization methods are based on time-consuming cycle-accurate simulation, unsuitable for analysis of hundreds of thousands of design options that is required to arrive at Pareto-optimal designs. This dissertation bridges the gap between a large design space of superscalar processors and the inability of cycle-accurate simulation to analyze a large design space, by providing a computationally and conceptually simple analytical method for generating Pareto-optimal superscalar processor designs. The proposed and evaluated analytical method consists of three parts: (1) a method for analytically estimating the performance in terms a cycles-per-instruction (CPI) using the application program statistics and the superscalar processor parameters, (2) a method of analytically estimating various energy consuming activities using the application program statistics and the superscalar processor parameters, and (3) a search method for systematically finding the Pareto-optimal designs. At the heart of these three parts are analytical equations that model the fundamental governing principles of superscalar processors. These equations are simple yet accurate enough to quickly find the Pareto-optimal superscalar processor designs. In addition to the computational simplicity, the analytical design optimization method is conceptually simple. It gives clear design guidance by providing (1) the ability to visualize the performance degrading events, such as branch mispredictions and instruction cache misses, (2) the ability to analyze energy consuming activity at the microarchitecture level, and (3) the cause-and-effect relationship between superscalar core design parameters. The conceptual simplicity allows a quick grasp of the analytical method and also provides key insights into the inner workings of superscalar processors. Overall the proposed analytical design optimization method can provide future SOC designers with an automated approach for generating Pareto-optimal application-specific superscalar processors with minimal design time and effort.

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