Association rule mining with the Micron Automata Processor

Abstract—Association rule mining (ARM) is a widely used data mining technique for discovering sets of frequently as-sociated items in large databases. As datasets grow in size and real-time analysis becomes important, the performance of ARM implementation can impede its applicability. We accelerate ARM by using Micron’s Automata Processor (AP), a hardware implementation of non-deterministic finite automata (NFAs), with additional features that significantly expand the APs capabilities beyond those of traditional NFAs. The Apriori algorithm that ARM uses for discovering itemsets maps naturally to the massive parallelism of the AP. We implement the multipass pruning strategy used in the Apriori ARM through the APs symbol replacement capability, a form of lightweight reconfigurability. Up to 129X and 49X speedups are achieved by the AP-accelerated Apriori on seven synthetic and real-world datasets, when compared with the Apriori single-core CPU implementation and Eclat, a more efficient ARM algorithm, 6-core multicore CPU implementation, respectively. The AP-accelerated Apriori solution also outperforms GPU im-plementations of Eclat especially for large datasets. Technology scaling projections suggest even better speedups from future generations of AP. Keywords-Automata Processor; association rule mining; fre-quent set mining I