FPGA Implementations of Neural Networks

1. Front Matter

   Pages i-xii

   PDF

2. FPGA Neurocomputers

   • Amos R. Omondi, Jagath C. Rajapakse, Mariusz Bajger

   Pages 1-36

3. On the Arithmetic Precision for Implementing Back-Propagation Networks on FPGA: A Case Study

   • Medhat Moussa, Shawki Areibi, Kristian Nichols

   Pages 37-61

4. FPNA: Concepts and Properties

   • Bernard Girau

   Pages 63-101

5. FPNA: Applications and Implementations

   • Bernard Girau

   Pages 103-136

6. Back-Propagation Algorithm Achieving 5 Gops on the Virtex-E

   • Kolin Paul, Sanjay Rajopadhye

   Pages 137-165

7. FPGA Implementation of Very Large Associative Memories

   • Dan Hammerstrom, Changjian Gao, Shaojuan Zhu, Mike Butts

   Pages 167-195

8. FPGA Implementations of Neocognitrons

   • Alessandro Noriaki Ide, José Hiroki Saito

   Pages 197-224

9. Self Organizing Feature Map for Color Quantization on FPGA

   • Chip-Hong Chang, Menon Shibu, Rui Xiao

   Pages 225-245

10. Implementation of Self-Organizing Feature Maps in Reconfigurable Hardware

    • Mario Porrmann, Ulf Witkowski, Ulrich Rückert

    Pages 247-269

11. FPGA Implementation of a Fully and Partially Connected MLP

    • Antonio Canas, Eva M. Ortigosa, Eduardo Ros, Pilar M. Ortigosa

    Pages 271-296

12. FPGA Implementation of Non-Linear Predictors

    • Rafael Gadea-Girones, Agustn Ramrez-Agundis

    Pages 297-323

13. The REMAP Reconfigurable Architecture: A Retrospective

    • Lars Bengtsson, Arne Linde, Tomas Nordstrom, Bertil Svensson, Mikael Taveniku

    Pages 325-360

During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

• Consumer Electronics
• Performance
• Text
• algorithms
• artificial intelligence
• integrated circuit
• metal-oxide-semiconductor transistor
• static-induction transistor
• tools

• Flinders University, Adelaide, Australia

  Amos R. Omondi

• Nanyang Tecnological University, Singapore

  Jagath C. Rajapakse

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