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. Since traditional architectures, such as CPU and GPU are not inherently suited for executing NFAs, special-purpose architectures are required for accelerating NFAs.
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.
Our paper surveys 50+ techniques that propose architectural optimizations to AP and use it for accelerating problems from various application domains such as bioinformatics, data-mining, machine learning, natural language processing, high-energy physics, and graph analytics. PDF is here
It is noteworthy that Micron has recently stopped developing AP. Instead, other companies such as Natural Intelligence Semiconductor (http://naturalsemi.com) and some academic research centers (such Center for Automata Processing at the University of Virginia) are leading the development and market-adoption of AP.
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.
Our paper surveys 50+ techniques that propose architectural optimizations to AP and use it for accelerating problems from various application domains such as bioinformatics, data-mining, machine learning, natural language processing, high-energy physics, and graph analytics. PDF is here
It is noteworthy that Micron has recently stopped developing AP. Instead, other companies such as Natural Intelligence Semiconductor (http://naturalsemi.com) and some academic research centers (such Center for Automata Processing at the University of Virginia) are leading the development and market-adoption of AP.
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