Kevin Skadron, Mircea Stan, Westley Weimer and Ahmed Abbasi receive $875,000 from NSF

EN-CS XPS:FULL: New Abstractions and Applications for Automata Computing

As society collects more and more data about the world around us, and digitizes more and more artifacts, “big data” promises unparalleled potential, but also poses new and unique computational challenges. Turning data into useful knowledge at or near real-time can have significant impacts, such as enabling timely intervention in healthcare and fast response in cybersecurity. As technology constraints limit CPU performance, researchers and practitioners are increasingly looking to specialized processors to accelerate data analytics. The ability to extract patterns from unstructured data is an especially important task. This research project carries out a cross-stack investigation to evaluate the effectiveness of the automata computing paradigm to accelerate pattern mining of unstructured data.

Specifically, by leveraging the industry’s new Automata Processor (developed by Micron Technology), this project is (1) developing benchmark suites of truly diverse automata for performance comparison of real and simulated, existing and future automata engines, (2) developing new tools, including programming languages, systems, and architectural enhancement to make automata computing intuitive and easy to adopt, (3) evaluating automata computing solutions to address real-world big-data applications, and (4) developing a set of educational and community-building activities to maximize the broader impact of the project outcome. Successful implementation of this project enable new automata-based abstractions to shed light on the performance of AP technology for various applications, such as pattern mining. This project will build the the intellectual foundations to support and catalyze research, education, training, and adoption of automata-based solutions to address big-data challenges in industry, government, and society.

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