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基于模拟退火算法的浮点转定点自动位宽优化工具 被引量:3

Automatic Word-Length Determination Tool Based on Simulated Annealing Algorithm
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摘要 开发了一套浮点转定点自动位宽优化软件系统(SATRANS),能够将用户输入的描述目标系统的浮点程序自动转换为位宽可配置的定点程序,并基于模拟退火算法进行自动位宽搜索,以得到满足精度要求的操作数定点位宽组合.同时,以IIR数字滤波器为例对SATRANS进行了实现与验证.结果表明,SATRANS的搜索结果优于传统贪心算法的搜索结果,并能够获得一系列满足精度要求的解,从而使得芯片设计人员能够在精度与复杂度等要素之间加以权衡,并选择一组最合适的位宽组合而用于芯片设计中.选择搜索结果中的面积最优解来配置IIR系统并在XilinxVirtex-6FPGA芯片中实现,相对于IEEE浮点单/双精度系统,其性能分别提高了12.4%和62.8%,面积的降幅分别为93.9%和97.9%. An automatic word-length determination tool (SATRANS) based on the simulated annealing al- gorithm was developed. SATRANS can automatically transform the system from floating-point model to fixed-point model and provide a series of word-length solutions that form a tradeoff curve for hardware complexity vs. signal quality. SATRANS was demonstrated to find word-length for an infinite impulse re- sponse filter (IIR). The results show that SATRANS can provide better word-length solution in compari- son to the traditional search method based on greedy strategy. The word-length optimized IIR targeting Xilinx Virtex-6 FPGA device was implemented, which improves the performance by 12.4% and 62.8% while saves almost 93.9 %and 97.9 % of area in comparison to the IEEE single and double floating-point generators.
出处 《上海交通大学学报》 EI CAS CSCD 北大核心 2013年第1期76-80,85,共6页 Journal of Shanghai Jiaotong University
基金 国家自然科学基金(60970036 61103011) 教育部博士点基金(20114307110001) 国家"核高基"重大专项(2009ZX01028-002-002)资助项目
关键词 位宽优化 模拟退火算法 浮点转定点 数字滤波器 word-length optimization simulated annealing algorithm iloating-point to tixed-point inii-nite impulse response filter
作者简介 黎渊(1984-)。男,江西省赣州市人,博士生,研究方向为计算机系统结构、高性能微处理器设计. 蒋江(联系人),男,副教授,电话(Tel.):13873118762;E-mail:csliyuan@hotmail.com.
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参考文献15

  • 1Keding H,Willems M,Coors M. FRIDGE:A fixed-point design and simulation environment[A].Paris:IEEE,1998.429-435.
  • 2Sung W,Kurn K. Simulation-based word-length optimization method for fixed-point digital signal processing systems[J].IEEE Transactions on Signal Processing,1995,(12):3087-3090.
  • 3Han K,Eo I,Kim K. Numerical word length optimization for CDMA demodulator[A].Sydney:IEEE,2001.290-300.
  • 4Cantin M A,Savaria Y,Lavoie P. A comparison of automatic word length optimization procedures[A].USA,IEEE Press,2002.612-615.
  • 5Kirkpatrick S,Vecchi M. Optimization by simulated annealing[J].Science,1983.671-680.
  • 6罗勇军,石明洪,白英彩.基于模拟退火的多约束路径优化选择算法[J].上海交通大学学报,2005,39(4):585-589. 被引量:8
  • 7谢传泉,何晨.混沌神经网络模型中的模拟退火策略[J].上海交通大学学报,2003,37(3):323-326. 被引量:22
  • 8吴浩扬,常炳国,朱长纯,刘君华.基于模拟退火机制的多种群并行遗传算法[J].软件学报,2000,11(3):416-420. 被引量:61
  • 9The MathWorks Inc. Fixed point toolbox user's guide[DB/OL].http://www.mathworks.com,2012.
  • 10Kim S,Kum K,Sung W. Fixed-point optimization utility for C and C++ based digital signal processing programs[J].IEEE Transactions on Circuits and Systems,1998,(11):1455-1464.

二级参考文献15

  • 1王雪梅,王义和.模拟退火算法与遗传算法的结合[J].计算机学报,1997,20(4):381-384. 被引量:123
  • 2Turgay K, Marwan K. Multi-constrained optimal path selection[A]. IEEE Computer Society, IEEE INFOCOMM [C]. Las Vegas, Nevada: Institute of Electrical Electronics Engineers Inc, 2001. 834-843.
  • 3Neve H, Mieghem P. TAMCRA: A tunable accuracy multiple constraints routing algorithm[J]. Computer Communications, 2000,23(11):667-679.
  • 4Chen S, Nahrstedt K. On finding multi-constrained paths[A]. IEEE ICC'98 [C]. Atlanta: Piscataway,1998.874-879.
  • 5Calvert K, Doar M, Zegura E. Modeling internet topology [J]. IEEE Communications Magazine,1997, 35(6):157-164.
  • 6Chong E, Maddila S, Morley S. On finding singlesource single-destination k shortest paths[A]. Seventh International Conference on Computing and Information [C]. Peterborough, Ontario Canada:Trent University, 1995.40-47.
  • 7Waxman B. Routing of multipoint connections [J].IEEE Journal on Selected Areas in Communications,1988, 30(12):1617-1622.
  • 8Chen L N, Aihara K. Chaotic simulated annealing by a neural network model with transient chaos[J]. Neural Networks, 1995, 8(6): 915-930.
  • 9Wang B Y, He Z Y, Nie J N. To implement the CDMA multiuser detector by using transsiently chaotic neural networks[J]. IEEE Transactions on Aerospace and Electronic Systems, 1997, 33(3): 1068- 1071.
  • 10Tokuda I, Aihara K, Nagashima T. Adaptive annealing for chaotic optimization [J]. Physical Review E,1998, 58(4): 5157-5160.

共引文献88

同被引文献20

  • 1高尚,杨静宇.混沌粒子群优化算法研究[J].模式识别与人工智能,2006,19(2):266-270. 被引量:76
  • 2Antamoshkin, Alexander N, Kazakovtsev, et al. Random search algorithm for the p-median problem[ J ]. Informatica ( Slovenia), 2013,37(3) :267-278.
  • 3Eberhart R C, Kennedy J. A new optimizer using particles swarm theory [ C ]//Proc Sixth International Symposium on Micro Machine and Human Science. Nagoya,Japan: IEEE Press, 1995 : 39-43.
  • 4Shi Y H, Eberhart R C. A modified particle swarm optimizer[ C ]//IEEE International Conference on Evolutionary Computa- tion. Anchorage. Alaska: IEEE Press, 1998 : 69-73.
  • 5Kennedy J, Eherhart R. Particle swarm optimization [ C ]//Proc IEEE International Conference on Neural Networks. Perth : IEEE Press, 1995 : 1 942-1 948.
  • 6ELGAMEL S A, SORAGHAN J J. Enhanced Mono- pulse Radar Tracking Using Filtering in Fractional Fourier Domain[C] // 2010 IEEE International Radar Conference, [S. l.] :[s. n. ], 2010:247-250.
  • 7SKOLNIK M I.雷达系统导论[M].3版.左群声,徐国良,马林,等译.北京:电子工业出版社,2006:56-62.
  • 8中国电子科技集团公司第三十八研究所.BWDSPl00软件用户手册[M].合肥:中国电子科技集团公司第三十八研究所,2011.
  • 9ARANDI S, EVRIPIDOU P. Programming Multi- Core Architectures Using Data-Flow Techniques[C] //2010 International Conference on Embedded Com- puter Systems, Samos:IEEE, 2010:152-161.
  • 10Antamoshkin A N,Kazakovtsev L A. Random search algorithm for the p-median problem[J]. Informatica (Slovenia), 2013, 37 (3) :267-278.

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