Selecting Representative Benchmark Inputs for Exploring Microprocessor Design Spaces

Abstract

The design process of a microprocessor requires representative workloads to steer the search process toward an optimum design point for the target application domain. However, considering a broad set of workloads to cover the large space of potential workloads is infeasible given how time-consuming design space exploration typically is. Hence, it is crucial to select a small yet representative set of workloads, which leads to a shorter design cycle while yielding a (near) optimal design. Prior work has mostly looked into selecting representative benchmarks; however, limited attention was given to the selection of benchmark inputs and how this affects workload representativeness during design space exploration. Using a set of 1,000 inputs for a number of embedded benchmarks and a design space with around 1,700 design points, we find that selecting a single or three random input(s) per benchmark potentially (in a worst-case scenario) leads to a suboptimal design that is 56% and 33% off, on average, relative to the optimal design in our design space in terms of Energy-Delay Product (EDP). We then propose and evaluate a number of methods for selecting representative inputs and show that we can find the optimum design point with as few as three inputs. © 2014, ACM. All rights reserved.