Biological neural networks continue to inspire new developments in algorithms and
microelectronic hardware to solve challenging data processing and classification problems …

Finite State Automata are widely used to accelerate pattern matching in many emerging
application domains like DNA sequencing and XML parsing. Conventional CPUs and …

High-performance automata-processing engines are traditionally evaluated using a limited
set of regular expression-rulesets. While regular expression rulesets are valid real-world …

A variety of applications employ ensemble learning models, using a collection of decision
trees, to quickly and accurately classify an input based on its vector of features. In this paper …

High-throughput and concurrent processing of thousands of patterns on each byte of an
input stream is critical for many applications with real-time processing needs, such as …

Finite State Machines (FSM) are widely used computation models for many application
domains. These embarrassingly sequential applications with irregular memory access …

Regular expressions have been widely used in various application domains such as
network security, machine learning, and natural language processing. Increasing demand …

Non-deterministic Finite Automata (NFA) are space-efficient finite state machines that have
significant applications in domains such as pattern matching and data analytics. In this …

Part-of-speech (POS) tagging is the foundation of many natural language processing
applications. Rule-based POS tagging is a wellknown solution, which assigns tags to the …

Automata Processing is an important kernel for many application domains, and is
challenging to accelerate using general purpose, von Neumann computers. New research …