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High Performance Stream Processing on FPGA

  • John McAllister
Chapter

Abstract

Field Programmable Gate Array (FPGA) have plentiful computational, communication and member bandwidth resources which may be combined into high-performance, low-cost accelerators for computationally demanding operations. However, deriving efficient accelerators currently requires manual register transfer level design—a highly time-consuming and unproductive process. Software-programmable processors are a promising way to alleviate this design burden but are unable to support performance and cost comparable to hand-crafted custom circuits. A novel type of processor is described which overcomes this shortcoming for streaming operations. It employs a fine-grained processor with very high levels of customisability and advanced program control and memory addressing capabilities in very large-scale custom multicore networks to enable accelerators whose performance and cost match those of hand-crafted custom circuits and well beyond comparable soft processors.

References

  1. 1.
    802.11 Working Group: IEEE P802.11ac/D2.2 Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 4: Enhancements for Very High Throughput for Operation in Bands below 6 GHz (2012)Google Scholar
  2. 2.
    Altera Inc.: Nios II Processor Reference Handbook (2014)Google Scholar
  3. 3.
    Altera Inc.: Stratix V Device Handbook (2014)Google Scholar
  4. 4.
    Antikainen, J., Salmela, P., Silven, O., Juntti, M., Takala, J., Myllyla, M.: Application-Specific Instruction Set Processor Implementation of List Sphere Detector. In: Conf. Record of the Forty-First Asilomar Conf. on Signals, Systems and Computers, 2007, pp. 943–947 (2007).  https://doi.org/10.1109/ACSSC.2007.4487358 Google Scholar
  5. 5.
    Barbero, L., Thompson, J.: Fixing the Complexity of the Sphere Decoder for MIMO Detection. IEEE Trans. Wireless Communications pp. 2131–2142 (2008).  https://doi.org/10.1109/TWC.2008.060378
  6. 6.
    Barbero, L.G., Thompson, J.S.: Rapid Prototyping of a Fixed-Throughput Sphere Decoder for MIMO Systems. In: IEEE Intl. Conf. on Communications, pp. 3082–3087 (2006).  https://doi.org/10.1109/ICC.2006.255278
  7. 7.
    Burg, A., Borgmann, M., Wenk, M., Zellweger, M., Fichtner, W., Bolcskei, H.: VLSI Implementation of MIMO Detection Using The Sphere Decoding Algorithm. IEEE Journal of Solid-State Circuits 40(7), 1566–1577 (2005).  https://doi.org/10.1109/JSSC.2005.847505 CrossRefGoogle Scholar
  8. 8.
    Cheah, H.Y., F., B., Fahmy, S., Maskell, D.L.: The iDEA DSP Block Based Soft Processor for FPGAs. ACM Trans. Reconfigurable Technol. Syst. 7(1) (2014)CrossRefGoogle Scholar
  9. 9.
    Chou, C.H., Severance, A., Brant, A.D., Liu, Z., Sant, S., Lemieux, G.G.: VEGAS: Soft Vector Processor with Scratchpad Memory. In: Proc. ACM/SIGDA Intl. Symp. Field Programmable Gate Arrays, FPGA ’11, pp. 15–24. ACM, New York, NY, USA (2011). https://doi.org/10.1145/1950413.1950420. URL http://doi.acm.org/10.1145/1950413.1950420
  10. 10.
    Chu, X., McAllister, J.: FPGA Based Soft-core SIMD Processing: A MIMO-OFDM Fixed-Complexity Sphere Decoder Case Study. In: IEEE Int. Conf. on Field-Programmable Technology (FPT), pp. 479–484 (2010).  https://doi.org/10.1109/FPT.2010.56814639
  11. 11.
    Chu, X., McAllister, J.: Software-Defined Sphere Decoding for FPGA-Based MIMO Detection. IEEE Transactions on Signal Processing 60(11), 6017–6026 (2012).  https://doi.org/10.1109/TSP.2012.2210951 MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hannig, F., Lari, V., Boppu, S., Tanase, A., Reiche, O.: Invasive Tightly-Coupled Processor Arrays: A Domain-Specific Architecture/Compiler Co-Design Approach. ACM Trans. Embed. Comput. Syst. 13(4s), 133:1–133:29 (2014). https://doi.org/10.1145/2584660 CrossRefGoogle Scholar
  13. 13.
    Hanzo, L., Webb, W., Keller, T.: Single and Multi-carrier Quadrature Amplitude Modulation: Principles and Applications for Personal Communications, WLANs and Broadcasting (2000)Google Scholar
  14. 14.
    IEEE802.11n: 802.11n-2009 IEEE Local and metropolitan area networks–Specific requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 5: Enhancements for Higher Throughput (2009).  https://doi.org/10.1109/IEEESTD.2009.5307322
  15. 15.
    Janhunen, J., Silven, O., Juntti, M., Myllyla, M.: Software Defined Radio Implementation of K-best List Sphere Detector Algorithm. In: Intl. Conf. on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), pp. 100–107 (2008).  https://doi.org/10.1109/ICSAMOS.2008.4664852
  16. 16.
    Li, M., Bougard, B., Xu, W., Novo, D., Van Der Perre, L., Catthoor, F.: Optimizing Near-ML MIMO Detector for SDR Baseband on Parallel Programmable Architectures. Design, Automation and Test in Europe (DATE) pp. 444–449 (2008).  https://doi.org/10.1109/DATE.2008.4484721
  17. 17.
    McAllister, J.: FPGA-based DSP. In: S.S. Bhattacharyya, E.F. Deprettere, R. Leupers, J. Takala (eds.) Handbook of Signal Processing Systems, 2nd edn., pp. 363–392. Springer US (2010)Google Scholar
  18. 18.
    Milder, P., Franchetti, F., Hoe, J.C., Püschel, M.: Computer Generation of Hardware for Linear Digital Signal Processing Transforms. ACM Trans. Des. Autom. Electron. Syst. 17(2), 15:1–15:33 (2012). https://doi.org/10.1145/2159542.2159547 CrossRefGoogle Scholar
  19. 19.
    Parhami, B.: Computer Arithmetic: Algorithms and Hardware Designs, 2nd edition edn. OUP USA (2010)Google Scholar
  20. 20.
    Pohst, M.: On The Computation of Lattice Vectors of Minimal Length, Successive Minima and Reduced Bases with Applications. SIGSAM Bull. 15(1), 37–44 (1981). http://doi.acm.org/10.1145/1089242.1089247 zbMATHGoogle Scholar
  21. 21.
    Qi, Q., Chakrabarti, C.: Parallel High Throughput Soft-output Sphere Decoder. In: IEEE Workshop on Signal Processing Systems (SIPS), pp. 174–179 (2010).  https://doi.org/10.1109/SIPS.2010.5624783
  22. 22.
    Ravindran, K., Satish, N., Jin, Y., Keutzer, K.: An FPGA-based soft multiprocessor system for IPv4 packet forwarding. In: Field Programmable Logic and Applications, 2005. International Conference on, pp. 487–492 (2005).  https://doi.org/10.1109/FPL.2005.1515769
  23. 23.
    Schnorr, C.P., Euchner, M.: Lattice Basis Reduction: Improved Practical Algorithms and Solving Subset Sum Problems. Mathematical Programming 66(1), 181–199 (1994)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Severance, A., Lemieux, G.: VENICE: A Compact Vector Processor for FPGA Applications. In: Field-Programmable Technology (FPT), 2012 Intl. Conf. on, pp. 261–268 (2012).  https://doi.org/10.1109/FPT.2012.6412146
  25. 25.
    Unnikrishnan, D., Zhao, J., Tessier, R.: Application specific customization and scalability of soft multiprocessors. In: Field Programmable Custom Computing Machines, 2009. FCCM ’09. 17th IEEE Symposium on, pp. 123–130 (2009).  https://doi.org/10.1109/FCCM.2009.41
  26. 26.
    Wolniansky, P., Foschini, G., Golden, G., Valenzuela, R.: V-BLAST: An Architecture for Realizing Very High Data Rates Over The Rich-Scattering Wireless Channel. In: 1998 URSI Int. Symp. Signals, Systems, and Electronics, pp. 295–300 (1998).  https://doi.org/10.1109/ISSSE.1998.738086
  27. 27.
    Wu, B., Masera, G.: A Novel VLSI Architecture of Fixed-Complexity Sphere Decoder. In: 13th Euromicro Conf. on Digital System Design: Architectures, Methods and Tools, pp. 737–744 (2010).  https://doi.org/10.1109/DSD.2010.10
  28. 28.
    Xilinx Inc.: LogiCORE IP CORDIC v4.0 (2011)Google Scholar
  29. 29.
    Xilinx Inc.: LogiCORE IP Fast Fourier Transform v7.1 (2011)Google Scholar
  30. 30.
    Xilinx Inc.: 7 Series DSP48E1 Slice User Guide (2013)Google Scholar
  31. 31.
    Xilinx Inc.: 7 Series FPGAs Memory Resources User Guide (2014)Google Scholar
  32. 32.
    Xilinx Inc.: MicroBlaze Processor Reference Guide (2014)Google Scholar
  33. 33.
    Yiannacouras, P., Steffan, J., Rose, J.: Portable, Flexible, and Scalable Soft Vector Processors. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on 20(8), 1429–1442 (2012).  https://doi.org/10.1109/TVLSI.2011.2160463 CrossRefGoogle Scholar
  34. 34.
    Yu, J., Eagleston, C., Chou, C.H., Perreault, M., Lemieux, G.: Vector Processing as a Soft Processor Accelerator. ACM Trans. Reconfigurable Technology and Systems 2(2) (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of ElectronicsCommunications and Information Technology (ECIT), Queen’s University BelfastBelfastUK

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