Entity Resolution Acceleration using Micron’s Automata Processor

Entity Resolution (ER), the process of finding identical entities across different databases, is critical to many information integration applications. As sizes of databases explode in the big-data era, it becomes computationally expensive to recognize identical entities for all possible records with variations allowed. Profiling results show that approximate matching is the primary bottleneck. Micron’s Automata Processor (AP), an efficient and scalable semiconductor architecture for parallel automata processing, provides a new opportunity for hardware acceleration for ER. We propose an AP-accelerated ER solution, which accelerates the performance bottleneck of fuzzy matching for similar but potentially inexactly-matched names, and use a real-world application to illustrate its effectiveness. Results show 121x to 4200x speedups for matching one record, with better accuracy (7.6% more correct pairs and 39% less generalized merge distance cost) over the existing CPU method.