Efficient Multiple Regular Matching on FPGAs based on E

Efficient Multiple Regular Matching on FPGAs based on E

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Why Microsoft Is Betting on FPGAs for Machine Learning at

With the once-regular improvements driven by Moore's Law diminishing and processor power demands climbing,Microsoft started using FPGAs in Bing and Azure infrastructure to accelerate its own workloads,like search indexing and software-defined networking,some years ago.However,the long-term plan was always to make hardware acceleration available to customers in some way.Scalable TCAM-based Regular Expression Matchingpattern matching.Early TCAM-based techniques [2,3] are based on the Aho-Corasick algorithm [6] and designed for plain string matching.Yu et al.[2] have proposed a TCAM-based multi-byte multiple-string matching algorithm with limited support for wildcards.Alicherry et al.[3] have proposed a state-encoding scheme for implementing anRevisiting Multiple Pattern Matching Algorithms for Multi A partition-based efficient algorithm for large scale multiple-strings matching.In Proc.the 12th Int.Conf.String Processing and Information Retrieval ,Buenos

Research Statement

have developed an FPGA based accelerator that can parallelize the regular expression matching,by testing the payload through the required rules simultaneously.Multiple regular expression rules are connected to the same input data stream,allowing a network payload toRegular expression matching with pipelined delayed input Regular expression matching (RE matching) is a widely used operation in network security monitoring applications.With the speed of network links increasing to 100 Gbps and 400 Gbps,it is necessary to speed up packet processing and provide RE matching at such high speeds.Regular Expression Matching Can Be Simple And FastTwo regular expressions can be alternated or concatenated to form a new regular expression if e 1 matches s and e 2 matches t,then e 1 |e 2 matches s or t,and e 1 e 2 matches st.The metacharacters * ,+ ,and ? are repetition operators e 1 * matches a sequence of zero or more (possibly different) strings,each of which match e 1 ; e 1

Real-time pattern matching with FPGAs Request PDF

This paper presents an efficient method for finding matches to a given regular expression in given text using FPGAs.To match a regular expression of length n,a serial machine requires 0(2^n Publications Center for Cognitive Computing SystemsFPGAs have been rapidly adopted for acceleration of Deep Neural Networks (DNNs) with improved latency and energy efficiency compared to CPU and GPU-based implementations.High-level synthesis (HLS) is an effective design flow for DNNs due to improved productivity,debugging,and design space exploration ability.Previous123456NextResearch Statementhave developed an FPGA based accelerator that can parallelize the regular expression matching,by testing the payload through the required rules simultaneously.Multiple regular expression rules are connected to the same input data stream,allowing a network payload to

Partial character decoding for improved regular expression

The designs achieve 1.6-3.2 Gbps throughput using 10-30% area of a large FPGA for matching over 1,500 regular expressions; that is 10-20x more efficient than previous FPGA-based works andNP-SARC Scalable network processing in the SARC multi Jan 01,2013 Efficient Multiple Regular Matching on FPGAs based on E#0183;Credit-based flow control 14 packet-based credits,and QoS support fault-tolerant,traffic priority support.For this level-2 switch,an efficient matching algorithm,called special sequential iterative matching (SIM),is proposed for Combined Input Output Queue (CIOQ) switches.LLVM-based automation of memory decoupling for OpenCL Feb 01,2020 Efficient Multiple Regular Matching on FPGAs based on E#0183;Despite the significant potential,OpenCL for FPGAs introduces a set of new design challenges.Throughput-oriented platforms (e.g.,GPUs) often rely on built-in schedulers to manage concurrent thread 1 execution at massive scale (over many cores) thus hiding memory latency.In contrast,FPGAs efficiency stems from a customized data-path,operation-level parallelism and also

LLVM-based automation of memory decoupling for OpenCL

Feb 01,2020 Efficient Multiple Regular Matching on FPGAs based on E#0183;Despite the significant potential,OpenCL for FPGAs introduces a set of new design challenges.Throughput-oriented platforms (e.g.,GPUs) often rely on built-in schedulers to manage concurrent thread 1 execution at massive scale (over many cores) thus hiding memory latency.In contrast,FPGAs efficiency stems from a customized data-path,operation-level parallelism and alsoIn-memory database acceleration on FPGAs a survey Oct 26,2019 Efficient Multiple Regular Matching on FPGAs based on E#0183;While FPGAs have seen prior use in database systems,in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons.First,specifically for in-memory databases,FPGAs integrated with conventional I/O provide insufficient bandwidth,limiting performance.Second,GPUs,which can also provide high throughput,Implementing Single-Chip FPGA Power SolutionsHigh Efficiency Across All Loads Ideal for low-power FPGAs,CPLDs,and application processors ENABLE 1 SDA SCL ENABLE 2 V IN = 2.7 V to 5.5V POR (Buck 1) VOUT2 = 1.8V to 3.3V Up to 600 mA VOUT1 = 0.8V to 2V Up to 600 mA I2C DVS interfa ce s ales power to match processor clock frequency Lowest Iq ( Efficient Multiple Regular Matching on FPGAs based on Elt;20 Efficient Multiple Regular Matching on FPGAs based on E#181;A) extends battery life Sp read spe ct um

Implementing Single-Chip FPGA Power Solutions

High Efficiency Across All Loads Ideal for low-power FPGAs,CPLDs,and application processors ENABLE 1 SDA SCL ENABLE 2 V IN = 2.7 V to 5.5V POR (Buck 1) VOUT2 = 1.8V to 3.3V Up to 600 mA VOUT1 = 0.8V to 2V Up to 600 mA I2C DVS interfa ce s ales power to match processor clock frequency Lowest Iq ( Efficient Multiple Regular Matching on FPGAs based on Elt;20 Efficient Multiple Regular Matching on FPGAs based on E#181;A) extends battery life Sp read spe ct um High performance FPGA and GPU complex pattern matching Aug 26,2014 Efficient Multiple Regular Matching on FPGAs based on E#0183;The wide and increasing availability of collected data in the form of trajectories has led to research advances in behavioral aspects of the monitored subjects (e.g.,wild animals,people,and vehicles).Using trajectory data harvested by devices,such as GPS,RFID and mobile devices,complex pattern queries can be posed to select trajectories based on specific events of interest.Fast regular expression matching using small TCAMs for Regular expression (RE) matching is a core component of deep packet inspection in modern networking and security devices.In this paper,we propose the first hardware-based RE matching approach that uses Ternary Content Addressable Memories (TCAMs),which are off-the-shelf chips and have been widely deployed in modern networking devices for packet classification.

Fast Regular Expression Matching using FPGAs Request PDF

This paper presents an efficient method for finding matches to a given regular expression in given text using FPGAs.To match a regular expression of length n,a serial machine requires 0(2^n Fast Regular Expression Matching Using FPGAsFeb 01,2001 Efficient Multiple Regular Matching on FPGAs based on E#0183;This paper presents an efficient method for finding matches to a given regular expression in given text using FPGAs.To match a regularFPGAs for AI (Artificial Intelligence) Key Possibilities FPGAs deserve a place among GPU and CPU-based AI chips for big data and machine learning.They show great potential for accelerating AI-related workloads,inferencing in particular.The main advantages of using an FPGA for accelerating machine learning and deep learning processes are their flexibility,custom parallelism,and the ability to be

Efficient discovery of longest-lasting correlation in

Jun 23,2016 Efficient Multiple Regular Matching on FPGAs based on E#0183;In addition,we study the use of a smart cache for disk-resident data (e.g.,millions of sequence objects) and a graph processing unit-based parallel processing technique for frequently updated data (e.g.,nonindexable streaming sequences)Efficient Stereoscopic Video Matching and Map Efficient Stereoscopic Video Matching and Map Reconstruction for a Wheeled Mobile Robot Regular Paper Oscar Montiel-Ross1,*,Roberto Sep Efficient Multiple Regular Matching on FPGAs based on E#250;lveda1,Oscar Castillo2 and Jorge Qui Efficient Multiple Regular Matching on FPGAs based on E#241;ones1 1 Instituto Polit Efficient Multiple Regular Matching on FPGAs based on E#233;cnico Nacional-CITEDI.Av.del Parque 1310,Tijuana,B.C.,M Efficient Multiple Regular Matching on FPGAs based on E#233;xi 2 Instituto Tecnol Efficient Multiple Regular Matching on FPGAs based on E#243;gico de Tijuana,M Efficient Multiple Regular Matching on FPGAs based on E#233;xi Av.Efficient Stereoscopic Video Matching and Map Efficient Stereoscopic Video Matching and Map Reconstruction for a Wheeled Mobile Robot Regular Paper Oscar Montiel-Ross1,*,Roberto Sep Efficient Multiple Regular Matching on FPGAs based on E#250;lveda1,Oscar Castillo2 and Jorge Qui Efficient Multiple Regular Matching on FPGAs based on E#241;ones1 1 Instituto Polit Efficient Multiple Regular Matching on FPGAs based on E#233;cnico Nacional-CITEDI.Av.del Parque 1310,Tijuana,B.C.,M Efficient Multiple Regular Matching on FPGAs based on E#233;xi 2 Instituto Tecnol Efficient Multiple Regular Matching on FPGAs based on E#243;gico de Tijuana,M Efficient Multiple Regular Matching on FPGAs based on E#233;xi Av.

Efficient Multiple Regular Matching on FPGAs based on E

Efficient Multiple Regular Expression Matching on FPGAs based on E tendedExtended SHIFTAND Method *Yusaku Kaneta,Shingo Yoshizawa,Shinichi Minato,HirokiArimura andYoshikazu Miyanaga [R48] ,(Graduate School of IST,Hokkaido University,Japan) Largescale pattern matching problem on hardwares A large number (e.g.thousands) of Demystifying automata processing GPUs,FPGAs or Micron'sR.Sidhu,and V.K.Prasanna,Fast Regular Expression Matching Using FPGAs, in Proc.of FCCM 2001.Google Scholar Digital Library; M.Becchi,and P.Crowley,Efficient regular expression evaluation theory to practice, in Proc.of ANCS 2008.Google Scholar Digital LibraryDemystifying Automata Processing GPUs,FPGAs orDemystifying Automata Processing GPUs,FPGAs or Microns AP? Marziyeh Nourian1,3,Xiang Wang1,Xiaodong Yu2,Wu-chun Feng2,Michela Becchi1,3 1,3Department of Electrical and Computer Engineering,2Department of Computer Science 1University of Missouri,2Virginia Tech,3North Carolina State University [email protected],[email protected],[email protected],[email protected],

Compact architecture for high-throughput regular

In this paper we present a novel architecture for high-speed and high-capacity regular expression matching (REM) on FPGA.The proposed REM architecture,based on nondeterministic finite automaton (RE-NFA),efficiently constructs regular expression matching engines (REME) of arbitrary regular patterns and character classes in a uniform structure,utilizing both logic slices and block memory Author Peter SuttonPartial Character Decoding for Improved RegularNFAs were an efficient method for implementing regular expressions in FPGAs.Both approaches in-volve a simple conversion process using one-hot en-coding of the states.As described in the following section,a number of researchers have examined regular expression matching in FPGAs.2.4.Regular Expression Matching in FPGAsApproximate reduction of finite automata for high-speed Our results provide experimental evidence that the method can be highly efficient in practice,allowing NIDSes to follow the rapid growth in the speed of networks.We consider the problem of approximate reduction of non-deterministic automata that appear in

A modular NFA architecture for regular expression matching

Y.-H.E.Yang,and V.Prasanna,Automatic Construction of Large-Scale Regular Expression Matching Engines on FPGA,2008 International Conference on Reconfigurable Computing and FPGAs Google Scholar Digital LibraryA Real-time Updatable FPGA-based Architecture for Fast Jan 01,2014 Efficient Multiple Regular Matching on FPGAs based on E#0183;In recent years,FPGA-based regular expression matching technology has become a new research hotspot.This paper focuses on using FPGA to design a fast and efficient regular expression matching system.Traditionally,finite state machine (FSM) is used to implement regular expression matching.A Memory-Efficient and Modular Approach for String Request PDF A Memory-Efficient and Modular Approach for String Matching on FPGAs In Network Intrusion Detection Systems (NIDSs),string matching demands exceptionally high performance to match

A Memory Efficient Pattern Matching Scheme for Regular

Jan 01,2017 Efficient Multiple Regular Matching on FPGAs based on E#0183;In this paper,e propose a memory efficient regular expression matching algorithm called Failureless Segmented Finite Automata (FSFA) with an acceptable searching speed.In FSFA,We eliminate Kleene closures by using additional data structures to reduce a large amount of states.A Dynamically Reconfigured Multi-FPGA Network PlatformMalicious software has become a major threat to computer users on the Internet today.Security researchers need to gather and analyze large sample sets to develop effective countermeasures.The setting of honeypots,which emulate vulnerable applications,is one method to collect attack code.We have proposed a dedicated hardware architecture for honeypots which allows both high-speedA Dynamically Reconfigured Multi-FPGA Network PlatformMalicious software has become a major threat to computer users on the Internet today.Security researchers need to gather and analyze large sample sets to develop effective countermeasures.The setting of honeypots,which emulate vulnerable applications,is one method to collect attack code.We have proposed a dedicated hardware architecture for honeypots which allows both high-speed

12345NextHigh performance FPGA and GPU complex pattern matching

Aug 26,2014 Efficient Multiple Regular Matching on FPGAs based on E#0183;The wide and increasing availability of collected data in the form of trajectories has led to research advances in behavioral aspects of the monitored subjects (e.g.,wild animals,people,and vehicles).Using trajectory data harvested by devices,such as GPS,RFID and mobile devices,complex pattern queries can be posed to select trajectories based on specific events of interest.1 An Efcient I/O Architecture for RAM-based Content content-addressable-memory (RAM-based CAM) always suffers high latency.Two primary causes of such latency include (1) the compulsory erasing stage along with the writing stage and (2) the major difference in data width between the RAM-based CAM (e.g.,8-bit width) and the modern systems (e

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