{"id":{"repo_id":"uic","oai_identifier":"oai:figshare.com:article/32995673"},"canonical_url":"https://search.dev.ndltd.org/etd/uic/oai:figshare.com:article/32995673","repository":{"repo_id":"uic","name":"University of Illinois - Chicago","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"A Framework for the Design and Generation of Spatial MFSA Accelerators: The SPARX Approach","abstract":"The growing demand for high-throughput pattern matching in fields such as cybersecurity, bioinformatics, and natural language processing has intensified research into automata-based accelerators. Regular expressions, while expressive and versatile, impose significant computational overhead when evaluated over massive data streams. Traditional CPU and GPU solutions, despite vectorization and multi-threading, remain constrained by the sequential execution model of Von Neumann architectures. This thesis addresses these limitations by introducing SPARX, the first complete framework that translates the Multi-RE Finite State Automaton (MFSA) formalism into synthesizable spatial hardware. The work builds on iMFAnt, a software engine that merges multiple regular expressions into a unified automaton, reducing redundancy by identifying shared prefixes, suffixes, and internal sequences. The MFSA model enables simultaneous multi-regex evaluation within a single traversal of the input, achieving compactness and high algorithmic efficiency. While iMFAnt demonstrated remarkable software performance—reducing state counts by 72%, transitions by 39%, and improving throughput up to 6×, its potential for spatial acceleration had not yet been explored. SPARX bridges this gap as a complete framework that translates the MFSA model into spatial hardware. It integrates simulation, validation, hardware resource utilization prediction, and automatic hardware generation into a single flow, mapping merged automata onto reconfigurable logic to combine algorithmic compression with hardware-level concurrency.","abstract_html":"The growing demand for high-throughput pattern matching in fields such as cybersecurity, bioinformatics, and natural language processing has intensified research into automata-based accelerators. Regular expressions, while expressive and versatile, impose significant computational overhead when evaluated over massive data streams. Traditional CPU and GPU solutions, despite vectorization and multi-threading, remain constrained by the sequential execution model of Von Neumann architectures. This thesis addresses these limitations by introducing SPARX, the first complete framework that translates the Multi-RE Finite State Automaton (MFSA) formalism into synthesizable spatial hardware. The work builds on iMFAnt, a software engine that merges multiple regular expressions into a unified automaton, reducing redundancy by identifying shared prefixes, suffixes, and internal sequences. The MFSA model enables simultaneous multi-regex evaluation within a single traversal of the input, achieving compactness and high algorithmic efficiency. While iMFAnt demonstrated remarkable software performance—reducing state counts by 72%, transitions by 39%, and improving throughput up to 6×, its potential for spatial acceleration had not yet been explored. SPARX bridges this gap as a complete framework that translates the MFSA model into spatial hardware. It integrates simulation, validation, hardware resource utilization prediction, and automatic hardware generation into a single flow, mapping merged automata onto reconfigurable logic to combine algorithmic compression with hardware-level concurrency.","abstract_has_math":false,"creators":["Alessandro Aldo Marina (24400601)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T00:00:00Z","date_published":"2026-05-01T00:00:00Z","updated_at":"2026-07-27T21:33:56Z","subjects":["Computer Architecture","Computer Science","Electrical and Computer Engineering"],"languages":[],"rights":["In Copyright","Open Access after 2031-05-01"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.25417/uic.32995673.v1","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Alessandro Aldo Marina (24400601)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-05-01T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/A_Framework_for_the_Design_and_Generation_of_Spatial_MFSA_Accelerators_The_SPARX_Approach/32995673"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Architecture","Computer Science","Electrical and Computer Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright","Open Access after 2031-05-01"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.25417/uic.32995673.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The growing demand for high-throughput pattern matching in fields such as cybersecurity, bioinformatics, and natural language processing has intensified research into automata-based accelerators. Regular expressions, while expressive and versatile, impose significant computational overhead when evaluated over massive data streams. Traditional CPU and GPU solutions, despite vectorization and multi-threading, remain constrained by the sequential execution model of Von Neumann architectures. This thesis addresses these limitations by introducing SPARX, the first complete framework that translates the Multi-RE Finite State Automaton (MFSA) formalism into synthesizable spatial hardware. The work builds on iMFAnt, a software engine that merges multiple regular expressions into a unified automaton, reducing redundancy by identifying shared prefixes, suffixes, and internal sequences. The MFSA model enables simultaneous multi-regex evaluation within a single traversal of the input, achieving compactness and high algorithmic efficiency. While iMFAnt demonstrated remarkable software performance—reducing state counts by 72%, transitions by 39%, and improving throughput up to 6×, its potential for spatial acceleration had not yet been explored. SPARX bridges this gap as a complete framework that translates the MFSA model into spatial hardware. It integrates simulation, validation, hardware resource utilization prediction, and automatic hardware generation into a single flow, mapping merged automata onto reconfigurable logic to combine algorithmic compression with hardware-level concurrency."]},{"key":"dc:title","label":"Title","values":["A Framework for the Design and Generation of Spatial MFSA Accelerators: The SPARX Approach"]}]}],"canonical_facts":{"dc:creator":["Alessandro Aldo Marina (24400601)"],"dc:date":["2026-05-01T00:00:00Z"],"dc:description":["The growing demand for high-throughput pattern matching in fields such as cybersecurity, bioinformatics, and natural language processing has intensified research into automata-based accelerators. Regular expressions, while expressive and versatile, impose significant computational overhead when evaluated over massive data streams. Traditional CPU and GPU solutions, despite vectorization and multi-threading, remain constrained by the sequential execution model of Von Neumann architectures. This thesis addresses these limitations by introducing SPARX, the first complete framework that translates the Multi-RE Finite State Automaton (MFSA) formalism into synthesizable spatial hardware. The work builds on iMFAnt, a software engine that merges multiple regular expressions into a unified automaton, reducing redundancy by identifying shared prefixes, suffixes, and internal sequences. The MFSA model enables simultaneous multi-regex evaluation within a single traversal of the input, achieving compactness and high algorithmic efficiency. While iMFAnt demonstrated remarkable software performance—reducing state counts by 72%, transitions by 39%, and improving throughput up to 6×, its potential for spatial acceleration had not yet been explored. 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