TiReX: Tiled Regular eXpression Matching Architecture

Comodi, Alessandro; Conficconi, Davide; Scolari, Alberto; Santambrogio, Marco D.

doi:10.1109/ipdpsw.2018.00028

Cited by 12 publications

(11 citation statements)

References 17 publications

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“…We move then to Neorv32 [33], a VHDL-based RISC-V core, where we apply the power of two restrictions to explore core memories space. Finally, we explore a VHDL-based Architecture called TiReX [34] where we explore both the datapath and memories parameter spaces.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

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Dovado: An Open-Source Design Space Exploration Framework

Paletti

Conficconi

Santambrogio

2021

2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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Traditional hardware development exploits description languages such as VHDL and (System)Verilog to produce highly parametrizable RTL designs. Different parameter values yield different utilization-frequency trade-offs, and handtuning is not feasible with a non-trivial amount of parameters. Generally, the Computer-Aided Design (CAD) literature proposes approaches that mainly tackle automatic exploration without combining a design automation feature. Hence, this work proposes Dovado, an open-source CAD tool for design space exploration (DSE) tailored for FPGAs-based designs. Starting from VHDL/(System)Verilog, Dovado exploits Vivado and supports the hardware developer for an exact exploration of a given set of parameters or a DSE where it returns the nondominated set of configuration points. In this work, we exploit a multi-objective integer formulation and Non-Dominated Sorting Genetic Algorithm (NSGA)-II for a fast DSE. Moreover, we propose an approximation model for the NSGA-II fitness function to decide whether Vivado or a Nadaraya-Watson model should estimate the optimization metrics.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…TiReX [34] is a domain-specific architecture tailored for the Regular Expressions domain. We explore the architecture scaling according to datapath and memories parameters, targeting a Zynq Ultrascale+ XCZU3EG and the same Kintex-7 for the implementation phase.…”

Section: Tirex: Vhdl Dsa For Regular Expressionsmentioning

confidence: 99%

Dovado: An Open-Source Design Space Exploration Framework

Paletti

Conficconi

Santambrogio

2021

2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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“…They encode the DFA as a set of rules into the memory of a softwareprogrammable DFA engine by exploiting the B-FSM concept for reprogrammable DFA [34]. Comodi et al [8] present an architecture that exploits a VLIW-like approach for a DFA with backtracking execution mode. All these methods are based on the DFA representation, which grows exponentially with the complexity of the RE.…”

Section: Related Workmentioning

confidence: 99%

“…RE matching is an essential kernel [2] for traditional computer security [32,41] and database queries [19,31] but also for novel domains such as natural language processing [35,42], and genomeprotein matching [8,36]. The literature contains different algorithms to tackle RE matching.…”

Section: Introductionmentioning

confidence: 99%

CICERO: A Domain-Specific Architecture for Efficient Regular Expression Matching

Parravicini

Conficconi

Sozzo

et al. 2021

ACM Trans. Embed. Comput. Syst.

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Regular Expression (RE) matching is a computational kernel used in several applications. Since RE complexity and data volumes are steadily increasing, hardware acceleration is gaining attention also for this problem. Existing approaches have limited flexibility as they require a different implementation for each RE. On the other hand, it is complex to map efficient RE representations like non-deterministic finite-state automata onto software-programmable engines or parallel architectures. In this work, we present CICERO , an end-to-end framework composed of a domain-specific architecture and a companion compilation framework for RE matching. Our solution is suitable for many applications, such as genomics/proteomics and natural language processing. CICERO aims at exploiting the intrinsic parallelism of non-deterministic representations of the REs. CICERO can trade-off accelerators’ efficiency and processors’ flexibility thanks to its programmable architecture and the compilation framework. We implemented CICERO prototypes on embedded FPGA achieving up to 28.6× and 20.8× more energy efficiency than embedded and mainstream processors, respectively. Since it is a programmable architecture, it can be implemented as a custom ASIC that is orders of magnitude more energy-efficient than mainstream processors.

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“…Alessandro Comodi et al, (IEEE 2018) [1], proposes a novel and efficient RE matching architecture for FPGAs, based on the concept of matching core. RE can be software-compiled into sequences of basic matching instructions that a matching core runs on input data, and can be replaced to change the RE to be matched.…”

Section: Review Of Literaturementioning

confidence: 99%

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Puri¹,

Patnaik²

2021

INDJCSE

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Machine learning supervised classification plays major role in large text classification. In health care data it contributes since couple of years for generating the privacy and security. Such kind of electric records might take large data on storage devices, so it needs to optimize with some processing techniques. The Natural Language Processing (NLP) features to represent text as a vector appropriate for use in machine learning algorithms. To describe text as just a vector suitable for use in machine learning algorithms, text classification techniques frequently rely on dictionary counters including bag-of-words as well NLP techniques. As an alternative feature set, editable search queries are proposed in this work. The suggested model utilizes the Smith Waterman (SW) optimization created by Pair -wise alignment to create a set of pattern matching characteristics based on labeled textual information or to train a later part classifier. The Naive Bayes (NB) supervised learning algorithm has used for classification. While a correlation of the special groups and traditional text characteristics shows that a classification algorithm does not make better decisions utilizing generated features, the produced factors can catch patterns that cannot be identified with collective expertise. As a result, it is significantly boost a classifier that combines conventional methods for generated characteristics. In the experimental analysis we evaluate the proposed system results with various existing systems that our system demonstrates effective results than traditional approaches.

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TiReX: Tiled Regular eXpression Matching Architecture

Cited by 12 publications

References 17 publications

Dovado: An Open-Source Design Space Exploration Framework

Dovado: An Open-Source Design Space Exploration Framework

CICERO: A Domain-Specific Architecture for Efficient Regular Expression Matching

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

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