Design space exploration aims to quickly determine the design parameters that yield the best results. In software, the designer must set algorithmic and performance parameters, e.g., thresholds, bounds, and other input parameters that provide the best output in terms of accuracy and runtime. In hardware design, the designer must determine parameters related to pipelining, memory architecture, and data types to give the best tradeoff between resource usage and performance. In both cases, one wants to quickly understand the relationship between the input and outputs and find the Pareto set of designs.

Sherlock is a DSE framework that can handle multiple conflicting optimization objectives and aggressively focuses on finding Pareto optimal solutions. Sherlock integrates a model selection process to choose the regression model that helps reach the optimal solution faster. Sherlock designs a strategy based around the Multi-Armed Bandit (MAB) problem, opting to balance exploration and exploitation based on the learned and expected results. Sherlock can decrease the importance of models that do not provide correct estimates, reaching the optimal design faster. Sherlock is capable of tailoring its choice of regression models to the problem at hand, leading to a model that best reflects the application design space

“Sherlock: A Multi-Objective Design Space Exploration Framework” was recently published in the ACM Transactions on Design Automation of Electronic Systems (TODAES). Congrats to the authors Quentin Gautier, Alric Althoff, Chris Crutchfield, and Ryan Kastner. The Sherlock algorithm was also released as open-source. We plan to use it in the future to tune machine learning models for optimized hardware implementations and tune algorithmic parameters for aerial tracking project. We hope that others will find is similarly useful!