Date of Award:

12-2025

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Mario Harper

Committee

Mario Harper

Committee

Charles Swenson

Committee

Shah Muhammad Hamdi

Abstract

Modern computers are powerful, but they are not always efficient enough for small, low power systems like those used on satellites and scientific instruments. To solve this problem, engineers often turn to FPGAs—reconfigurable computer chips that can be customized to run specific tasks much faster and with far less energy than ordinary processors. However, finding the best possible design for an FPGA program is extremely difficult because there are millions of ways a design could be built, and only a small fraction of them actually perform well.

This thesis presents SNOW, a new framework that helps automate the search for better FPGA designs. Instead of relying on guesswork or trial-and-error, SNOW uses an organized, multi-phase exploration process that tests many design possibilities, learns which ideas work best, and gradually focuses on the most promising areas. The framework breaks the search into manageable regions, deploys many “agents” to explore these regions in parallel, and uses statistical methods to guide the search toward better solutions over time.

By making this process faster and more efficient, SNOW reduces the time and expertise needed to produce high-quality FPGA implementations. This helps enable advanced computing on small, power-limited systems—such as CubeSats and other space missions—where every watt of power and every gram of hardware matters. The research demonstrates that structured exploration can significantly improve performance while lowering the cost and effort of developing FPGA-based computing systems.

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