The Center conducts fundamental research on foundations and applications of automata computing; the Automata Processor is a novel, massively parallel computational accelerator capable of 1-2 order-of-magnitude speedups within existing computer system form factors and power constraints.
The Center’s collaborative approach facilitates teaming and accelerates commercialization.Mission-driven agencies can partner with the Center to research how automata computing can address critical challenges, including NP-hard problems that are currently considered unsolvable. The Center’s partnership with UVa’s Applied Research Institute provides access to secure research facilities for conducting sensitive research and development.
Research areas include:
- algorithm development
- hybrid computing
- new programming languages
- biomedical informatics
- business/consumer informatics
- entity resolution
- graph analytics
- heirarchical temporal memory
- natural language processing
As an emerging computer scientist and an early-career computer architecture researcher, I am thrilled to be working on Micron’s new Automata Processor (AP). The opportunity to be the first to benchmark, evaluate and develop applications for an industry-new technology is extraordinary. I am drawn to the novelty of the AP’s unique MISD architecture. We have already successfully demonstrated performance superiority of this new processor for certain class of applications. I am very excited to continue my work exploring new capabilities for the AP.
Jack Wadden, Graduate Research Assistant
CAP Research Tools
An open-source, multi-architecture automata processing ecosystem with Docker image.
An Automata Simulator for the CPU.
A framework for accelerating automata on FPGAs.
A framework for accelerating DFAs on GPUs.
JSON Automata Representation
An open-source toolchain to design and evaluate island style spatial automata processing architectures.
Performance Evaluation of Regular Expression Matching Engines Across Different Computer Architectures
V. Dang, J. Wadden, M. El-Hadedy, X. Huang, K. Wang, M. Stan, and K. Skadron. SRC TechCon, Austin, TX, 2016 (TECHCON2016)
Entity resolution acceleration using Micron’s Automata Processor
C. Bo, K. Wang, J. Fox, and K. Skadron. SRC TechCon, Austin, TX, 2016 (TECHCON2016)
Sequential pattern mining with the Micron Automata Processor
K. Wang, E. Sadredini, K. Skadron. ACM International Conference on Computing Frontiers (CF 2016)
RAPID Programming of Pattern-Recognition Processors
K. Angstadt, W. Weimer, and K. Skadron. Proceedings of the ACM International Symposium on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2016, to appear)
Brill Tagging on the Micron Automata Processor
K. Zhou, K. Wang, J. Fox, and D. Brown. IEEE International Conference on Semantic Computing (ICSC 2015)
Entity Resolution using the Micron Automata Processor
C. Bo, K. Wang, J. Fox, and K. Skadron. 5th International Workshop on Architectures and Systems for Big Data (ASBD), in conjunction with the 42nd International Symposium on Computer Architecture (ISCA 2015).
Association Rule Mining with the Micron Automata Processor
K. Wang, M. Stan, and K. Skadron. 29th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2015).
Generating efficient and high-quality pseudo-random behavior on Automata Processors
J.Wadden, N. Brunelle, K. Wang, M. El-Hadedy, G. Robins, M. Stan, and K. Skadron. 2016 IEEE 344th International Conference on Computer Design (ICCD16)
An Overview of Micron’s Automata Processor
K. Wang, K. Angstadt, C. Bo, N. Brunelle, E. Sadredini, T. Tracy II, J. Wadden, M. R. Stan, and K. Skadron. Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Oct. 2016.
Frequent Subtree Mining on the Automata Processor: Challenges and Opportunities
E. Sadredini, K. Wang, and K. Skadron. Proceedings of the ACM International Conference on Supercomputing (ICS), June 2017.
Automata-to-Routing: An Open-Source Toolchain for Design-Space Exploration of Spatial Automata Processing Architectures
J. Wadden, Samira Khan, and K. Skadron. Proceedings of the IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), Apr. 2017.
MNRL and MNCaRT: An Open-Source, Multi-Architecture State Machine Research and Execution Ecosystem
K. Angstadt, J. P. Wadden, W. Weimer, and K. Skadron. Tech. Report CS-2017-01, Univ. of Virginia Dept. of Computer Science, May 2017
RAPID: Accelerating pattern search applications with reconfigurable hardware
K. Angstadt, J. Wadden, X. Huang, M. El-Hadedy, W. Weimer, and K. Skadron, SRC TechCon, Austin, TX, 2016 (TECHCON2016)
Toward machine learning on the Automata Processor
T. Tracy II, Y. Fu, I. Roy, E. Jonas, P. Glendenning. International Supercomputing Conference – High Performance Computing (ISC-HPC 2016).
Cellular Automata on the Micron Automata Processor
K. Wang and K. Skadron; University of Virginia Technical Report #CS-2015-03.
Nondeterministic Finite Automata in Hardware – the Case of the Levenshtein Automaton
T. Tracy, M. Stan, N. Brunelle, J. Wadden, K. Wang, K. Skadron, G. Robins. 5th International Workshop on Architectures and Systems for Big Data (ASBD), in conjunction with the 42nd International Symposium on Computer Architecture (ISCA 2015).
Regular expression acceleration on the micron automata processor: Brill tagging as a case study
Zhou et al; IEEE International Conference on Big Data (Big Data 2015)
Fast Track Pattern Recognition in High Energy Physics Experiments with the Automata Processor
M. Wang, , G. Cancelo, C. Greena, D. Guo, K. Wang, and T. Zmuda. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.
ANMLZoo: A benchmark Suite for exploring bottlenecks in Automata Processing engines and architectures
J. Wadden, V. Dang, N. Brunelle, T. Tracy, D. Guo, E. Sadredini, K. Wang, C. Bo, G. Robins, M. Stan, and K. Skadron. 2016 IEEE International Symposium on Workload Characterization (IISWC’16)
Feature Extraction and Image Retrieval on an Automata Structure
T. Ly, R. Sarkar, K. Skadron, and S. T. Acton. Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, Nov. 2016.
Entity Resolution Acceleration using Micron’s Automata Processor
C. Bo, K. Wang, J. Fox, and K. Skadron. Proceedings of the 2016 IEEE International Conference on Big Data (BigData), Dec. 2016.
Hierarchical Pattern Mining with the Micron Automata Processor
K. Wang, E. Sadredini, and K. Skadron. International Journal of Parallel Programming (IJPP), Jan. 2017.
Fast Searching for Potential gRNA Off-Target Sites for CRISPR/Cas9 using Automata Processing
C. Bo, E. Sadredini, and K. Skadron. SRC TechCon, Austin, TX, 2016 (TECHCON2017)
REAPR: Reconfigurable Engine for Automata Processing
T. Xie, V. Dang, J. Wadden, K. Skadron, and M. Stan. 27th International Conference on Field Programmable Logic and Applications (FPL 2017)
CAP White Papers
Our group has demonstrated tremendous application potential using Micron’s Automata Processor. Below are some example application areas that may be of interest to industry partners and government sponsors. In addition to leading expertise on (and to date, exclusive access to) AP technology, our team also has extensive experience and expertise with hardware acceleration in general.
If you are a company or research lab, and are interested in exploring how the AP and other hardware acceleration technologies can be applied to meet your needs, please contact us and we will respond shortly; and if appropriate, we will be more than happy to develop a white paper to address your needs.
The AP’s massively parallel operation allows it to quickly check for prescribed patterns and their variations. This capability enables the AP to be uniquely suited for cybersecurity applications such as packet inspection and attribution. We believe there is a wide range of applications for AP technology in industry and national defense.
The advent of Big Data bring unprecedented opportunities but also significant analytics challenges. The AP’s ability to quickly implement association rule mining (ARM) algorithms such as frequent itemset, sequential pattern mining, and frequent subtree mining can quickly identify relations and patterns in massive datasets. We also believe tremendously valuable insight can also be gained from open internet sources such as social media platforms. However, the volume, variety, velocity, and veracity challenges of internet open source data is a formidable challenge. The AP’s ability to speedup implementation of entity resolution (ER) can be leveraged to perform attribution on internet open source data. ARM and ER are a few examples of how we think the AP can be effectively applied for data reduction.
Approximate string matching/ Bioinformatics
Aligning DNA reads to a reference genome is a common and time consuming process. We have demonstrated significant speedups implementing DNA alignment on the AP. Furthermore, the AP’s NFA flexibility allows it to be very effective at tolerating variations (gaps, mutation, and insertions) in candidate patterns. This is capability is analogous to approximate string matching. That is, the AP is able to effectively match string patterns with varied edit distance. We envision many relevant research and industry applications based on this powerful capability.
Micron introduction of AP technology
UVA established the Center for Automata Processing
AP hardware available through the Center for Automata Processing