The Automata Processor (AP)
The Automata Processor (AP) is the first successful hardware implementation of a non-deterministic finite automata computing architecture that is especially well-suited for complex pattern matching on unstructured data. This video conceptually explains how the AP leverages the memory-based state machine approach to perform parallel data mining quickly and efficiently.
Center for Automata Processing - CAP Mission
The University of Virginia and Micron Technology, Inc. co-founded the Center for Automata Processing (CAP) to catalyze the growth of an ecosystem around automata processing. Micron’s Automata Processor, a hardware implementation of automata computing, is poised to dramatically accelerate solutions aimed at big data challenges.
CAP is a collaboration of universities, companies and government agencies. The Center’s objectives are to develop innovative technologies and applications that address industry, government and societal needs, and to train future data scientists and engineers in this groundbreaking approach to computing.
We aim to achieve these objectives through four core activities:
1. Enable collaborative research and education opportunities in the foundation and application of automata processing across universities and research laboratories.
2. Leverage AP technology to develop innovative applications that address industry, government, and societal needs.
3. Create a public-private open innovation research network to speed commercialization and adoption of new AP technologies.
4. Provide access to AP hardware, SDK, training, and research workshops.
“Although the Micron Automata Processor (AP) was originally designed for text-based searches, I immediately realized its potential in addressing other classes of pattern recognition problems in Experimental High Energy Physics (HEP). Over the past year, a number of us at Fermilab have been collaborating with researchers at the the University of Virginia’s Center for Automata Processing to explore the suitability of the AP in particle track finding applications in HEP experiments. We have successfully demonstrated a proof-of-concept and are now looking into its use in signal processing applications in data acquisition systems in HEP experiments.” – Dr. Michael Wang, Physicist at Fermilab