FPGA Implementations of Neural Networks - Softcover

Softcover

ISBN 10: 1441939423 ISBN 13: 9781441939425

Verlag: Springer, 2010

Inhaltsangabe

Preface. 1. FPGA Neurocomputers; A.R.Omondi, J.C.Rajapakse and M.Bajger. 2. Arithmetic precision for BP networks; M.Moussa, S.Areibi and K.Nichols. 3. FPNA: Concepts and properties; B.Girau. 4. FPNA: Applications and implementations; B.Girau. 5. Back-Propagation Algorithms Achieving 5 GOPS on the VirtexE; K.Paul and S.Rajopadhye. 6. FPGA Implementation of Very Large Associative Memories; D.Hammerstrom, C.Gao, S.Zhu and M.Butts. 7. FPGA Implementations of Neocognitrons; A.Noriaki Ide and J.Hiroki Saito. 8. Self Organizing Feature Map for Color Quantization on FPGA; C-H.Chang, M.Shibu and R.Xiao. 9. Implemention of Self-Organizing Feature Maps in Reconfigurable Hardware; M.Porrmann, U.Witkowski and U.Rückert. 10. FPGA Implementation of a Fully and Partially Connected MLP; A.Canas, E.M.Ortigosa, E.Ros and P.M.Ortigosa. 11. FPGA Implementation of Non-Linear Predictors; R.Gadea-Girones and A.Ramrez-Agundis. 12. The REMAP Reconfigurable Architecture: a retrospective; L.Bengtsson, A.Linde, T.Nordstrom, B.Svensson and M.Taveniku.

Über diesen Titel

Reseña del editor

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.

Reseña del editor

The development of neural networks has now reached the stage where they are employed in a large variety of practical contexts. However, to date the majority of such implementations have been in software. While it is generally recognised that hardware implementations could, through performance advantages, greatly increase the use of neural networks, to date the relatively high cost of developing Application-Specific Integrated Circuits (ASICs) has meant that only a small number of hardware neurocomputers has gone beyond the research-prototype stage. The situation has now changed dramatically: with the appearance of large, dense, highly parallel FPGA circuits it has now become possible to envisage putting large-scale neural networks in hardware, to get high performance at low costs. This in turn makes it practical to develop hardware neural-computing devices for a wide range of applications, ranging from embedded devices in high-volume/low-cost consumer electronics to large-scale stand-alone neurocomputers. Not surprisingly, therefore, research in the area has recently rapidly increased, and even sharper growth can be expected in the next decade or so. Nevertheless, the many opportunities offered by FPGAs also come with many challenges, since most of the existing body of knowledge is based on ASICs (which are not as constrained as FPGAs). These challenges range from the choice of data representation, to the implementation of specialized functions, through to the realization of massively parallel neural networks; and accompanying these are important secondary issues, such as development tools and technology transfer. All these issues are currently being investigated by a large number of researchers, who start from different bases and proceed by different methods, in such a way that there is no systematic core knowledge to start from, evaluate alternatives, validate claims, and so forth. FPGA Implementations of Neural Networks aims to be a timely one that fill this gap in three ways: First, it will contain appropriate foundational material and therefore be appropriate for advanced students or researchers new to the field. Second, it will capture the state of the art, in both depth and breadth and therefore be useful researchers currently active in the field. Third, it will cover directions for future research, i.e. embryonic areas as well as more speculative ones.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2010
• Sprache: Englisch
• ISBN 10: 1441939423
• ISBN 13: 9781441939425
• Einband: Tapa blanda
• Anzahl der Seiten: 372
• Herausgeber: Omondi Amos R., Rajapakse Jagath C.
• Kontakt zum Hersteller: Nicht verfügbar
• Verantwortliche Person: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Deutschland