University of Virginia Patent Foundation

Elaheh Sadredini

Kevin Skadron

Ke Wang

Gholamreza Rahimi

Mircea R Stan

Chunkun Bo

Vinh Dang

A hardware accelerated solution of the SPM (Sequential Pattern Mining) is proposed using Micron's Automata Processor (AP), a hardware implementation of non-deterministic finite automata (NFAs) The Generalized Sequential Pattern (GSP) algorithm for SPM searching exposes massive parallelism, and is therefore well-suited for AP acceleration. The multi puss pruning strategy of the GSP is implemented is the APs fast reconfigurability. A generalized automaton structure is proposed by flattening sequential patterns to simple strings to reduce compilation time and to minimize overhead of reconfiguration. Up to 90× and 29× speedups are achieved by the AP-accelerated GSP on six real-world datasets, when compared with the optimized multicore CPU (Central Processing Unit) and GPU (Graphics Processing Unit) GSP implementations, respectively. The proposed CPU-AP solution also outperforms the state-of-the-art PrefixSpan and SPADE (Sequential PAttern Discovery using Equivalence classes algorithms on multicore CPU by up to 452× and 49× speedups.

Sequential pattern mining with the micron automata processor

A method of operating a finite state machine circuit can be provided by determining if a target sequence of characters included in a string of reference characters occurs within a specified difference distance using states indicated by the finite state machine circuit to indicate a number of character mis-matches between the target sequence of characters and a respective sequence of characters within the string of reference characters.

Methods, circuits, systems, and articles of manufacture for searching a reference sequence for a target sequence within a specified distance

A method of searching tree-structured data can be provided by identifying all labels associated with nodes in a plurality of trees including the tree-structured data, determining which of the labels is included in a percentage of the plurality of trees that exceeds a frequent threshold value to provide frequent labels, defining frequent candidate sub-trees for searching within the plurality of trees using combinations of only the frequent labels, and then searching for the frequent candidate sub-trees in the plurality of trees including the tree-structured data using a plurality of pruning kernels instantiated on a non-deterministic finite state machine to provide a less than exact count of the frequent candidate sub-trees in the plurality of trees.

Methods, circuits, and articles of manufacture for frequent sub-tree mining using non-deterministic finite state machines

A finite state machine circuit can include a plurality of rows of gain cell embedded Dynamic Random Access Memory (GC-eDRAM) cells that can be configured to store state information representing all N states expressed by a finite state machine circuit. A number of eDRAM switch cells can be electrically coupled to the plurality of rows of the GC-eDRAM cells, where the number of eDRAM switch cells can be arranged in an M&#xd7;M cross-bar array where M is less than N, and the number of eDRAM switch cells can be configured to provide interconnect for all transitions between the all N states expressed by the finite state machine circuit.

Methods, circuits, systems, and articles of manufacture for state machine interconnect architecture using embedded DRAM

The present invention introduces the development of a flexible CPU-AP (Computer Processing Unit-Automata Processor) computing infrastructure for mining hierarchical patterns based on Apriori algorithm. A novel automaton design strategy, called linear design, is described to generate automata for matching and counting hierarchical patterns and apply it on SPM (Sequential Pattern Mining). In addition, another novel automaton design strategy, called reduction design, is described for the disjunctive rule matching (DRM) and counting. The present invention shows performance improvement of AP SPM and DRM solutions and broader capability over multicore and GPU (Graphics Processing Unit) implementations of GSP SPM, and shows that AP SPM and DRM solutions outperform state-of-the-art SPM algorithms SPADE and PrefixSpan (especially for larger datasets).

Growing community of inventors

Charlottesville, VA, United States of America

Elaheh Sadredini