Machine Learning with Micron’s Automata Processor

A new survey paper describing Micron’s Automata Processor (AP) was recently published. AP has many potential applications in data mining, bioinformatics, natural language processing, etc. Micron has recently stopped developing AP, however other companies such as Natural Intelligence Semiconductor, a spin-off from Micron) and some academic research centers (Center for Automata Processing at the University of Virginia) are leading the development and market-adoption of AP.

The paper from IIT Hyderabad in India, “A Survey on Applications and Architectural-optimizations of Micron’s Automata Processor,” surveys applications and architectural-optimizations of Micron’s Automata Processor.

Problems from a wide variety of application domains can be modeled as “nondeterministic finite automaton” (NFA) and hence, efficient execution of NFAs can improve the performance of several key applications. However, traditional architectures, such as CPU and GPU are not inherently suited for executing NFAs, and hence, special-purpose architectures are required for accelerating them. Micron’s automata processor (AP) exploits massively parallel in-memory processing capability of DRAM for executing NFAs and hence, it can provide orders of magnitude performance improvement compared to traditional architectures. The paper presents a survey of techniques that propose architectural optimizations to AP and use it for accelerating problems from various application domains. The paper will be useful not only for computer architects and processor-designers but also for researchers in the field of bioinformatics, data-mining, machine learning and others. For example, here is a research paper, “Association Rule Mining with the Micron Automata Processor,” that explores using the AP for machine learning applications. Here is another paper providing a general overview of the Micron AP.

Contributed by Daniel D. Gutierrez, Managing Editor and Resident
Data Scientist for insideBIGDATA. In addition to being a tech
journalist, Daniel also is a consultant in data scientist, author,
educator and sits on a number of advisory boards for various start-up
companies. 

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