{"title":"基于同构消去的片上网络启发式映射NoC优化算法","authors":"Weng Xiaodong, Liu Yi, Yang Yintang","doi":"10.1049/iet-cdt.2019.0212","DOIUrl":null,"url":null,"abstract":"<div>\n <p>With the development of network-on-chip (NoC) theory, lots of mapping algorithm have been proposed to solve the application mapping problem which is an NP-hard (non-polynomial hard) problem. Most algorithms are based on a heuristic algorithm. They are trapped by iterations limited, not by the distance between iterations, because of the isomorphism of mapping sequence. In this study, the authors define and analyse the isomorphism with the genetic algorithm (GA) which is a heuristic algorithm. Then, they proposed an approach called density direction transform algorithm to eliminate the isomorphism of mapping sequence and accelerate the convergence of population. To verify this approach, they developed a density-direction-based genetic mapping algorithm (DDGMAP) and make a comparison with genetic mapping algorithm (GMA). The experiment demonstrates that compared to the random algorithm, their algorithm (DDGMAP) can achieve on an average 23.48% delay reduction and 7.15% power reduction. And DDGMAP gets better performance than GA in searching the optimal solution.</p>\n </div>","PeriodicalId":50383,"journal":{"name":"IET Computers and Digital Techniques","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cdt.2019.0212","citationCount":"2","resultStr":"{\"title\":\"Network-on-chip heuristic mapping algorithm based on isomorphism elimination for NoC optimisation\",\"authors\":\"Weng Xiaodong, Liu Yi, Yang Yintang\",\"doi\":\"10.1049/iet-cdt.2019.0212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>With the development of network-on-chip (NoC) theory, lots of mapping algorithm have been proposed to solve the application mapping problem which is an NP-hard (non-polynomial hard) problem. Most algorithms are based on a heuristic algorithm. They are trapped by iterations limited, not by the distance between iterations, because of the isomorphism of mapping sequence. In this study, the authors define and analyse the isomorphism with the genetic algorithm (GA) which is a heuristic algorithm. Then, they proposed an approach called density direction transform algorithm to eliminate the isomorphism of mapping sequence and accelerate the convergence of population. To verify this approach, they developed a density-direction-based genetic mapping algorithm (DDGMAP) and make a comparison with genetic mapping algorithm (GMA). The experiment demonstrates that compared to the random algorithm, their algorithm (DDGMAP) can achieve on an average 23.48% delay reduction and 7.15% power reduction. And DDGMAP gets better performance than GA in searching the optimal solution.</p>\\n </div>\",\"PeriodicalId\":50383,\"journal\":{\"name\":\"IET Computers and Digital Techniques\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2020-09-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cdt.2019.0212\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Computers and Digital Techniques\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/iet-cdt.2019.0212\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Computers and Digital Techniques","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/iet-cdt.2019.0212","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Network-on-chip heuristic mapping algorithm based on isomorphism elimination for NoC optimisation
With the development of network-on-chip (NoC) theory, lots of mapping algorithm have been proposed to solve the application mapping problem which is an NP-hard (non-polynomial hard) problem. Most algorithms are based on a heuristic algorithm. They are trapped by iterations limited, not by the distance between iterations, because of the isomorphism of mapping sequence. In this study, the authors define and analyse the isomorphism with the genetic algorithm (GA) which is a heuristic algorithm. Then, they proposed an approach called density direction transform algorithm to eliminate the isomorphism of mapping sequence and accelerate the convergence of population. To verify this approach, they developed a density-direction-based genetic mapping algorithm (DDGMAP) and make a comparison with genetic mapping algorithm (GMA). The experiment demonstrates that compared to the random algorithm, their algorithm (DDGMAP) can achieve on an average 23.48% delay reduction and 7.15% power reduction. And DDGMAP gets better performance than GA in searching the optimal solution.
期刊介绍：
IET Computers & Digital Techniques publishes technical papers describing recent research and development work in all aspects of digital system-on-chip design and test of electronic and embedded systems, including the development of design automation tools (methodologies, algorithms and architectures). Papers based on the problems associated with the scaling down of CMOS technology are particularly welcome. It is aimed at researchers, engineers and educators in the fields of computer and digital systems design and test.
The key subject areas of interest are:
Design Methods and Tools: CAD/EDA tools, hardware description languages, high-level and architectural synthesis, hardware/software co-design, platform-based design, 3D stacking and circuit design, system on-chip architectures and IP cores, embedded systems, logic synthesis, low-power design and power optimisation.
Simulation, Test and Validation: electrical and timing simulation, simulation based verification, hardware/software co-simulation and validation, mixed-domain technology modelling and simulation, post-silicon validation, power analysis and estimation, interconnect modelling and signal integrity analysis, hardware trust and security, design-for-testability, embedded core testing, system-on-chip testing, on-line testing, automatic test generation and delay testing, low-power testing, reliability, fault modelling and fault tolerance.
Processor and System Architectures: many-core systems, general-purpose and application specific processors, computational arithmetic for DSP applications, arithmetic and logic units, cache memories, memory management, co-processors and accelerators, systems and networks on chip, embedded cores, platforms, multiprocessors, distributed systems, communication protocols and low-power issues.
Configurable Computing: embedded cores, FPGAs, rapid prototyping, adaptive computing, evolvable and statically and dynamically reconfigurable and reprogrammable systems, reconfigurable hardware.
Design for variability, power and aging: design methods for variability, power and aging aware design, memories, FPGAs, IP components, 3D stacking, energy harvesting.
Case Studies: emerging applications, applications in industrial designs, and design frameworks.
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