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008			150903s2006 xxu| o |||| 0|eng d
020			_a9780387284873 _9978-0-387-28487-3
024	7		_a10.1007/0387284877 _2doi
035			_avtls000330549
039		9	_a201509030723 _bVLOAD _c201404120503 _dVLOAD _c201404090244 _dVLOAD _c201401311345 _dstaff _y201401291458 _zstaff _wmsplit0.mrc _x969
050		4	_aTK7888.4
100	1		_aOmondi, Amos R. _eeditor. _9302997
245	1	0	_aFPGA Implementations of Neural Networks / _cedited by Amos R. Omondi, Jagath C. Rajapakse.
264		1	_aBoston, MA : _bSpringer US, _c2006.
300			_aXII, 360 páginas, _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aFPGA Neurocomputers -- On the Arithmetic Precision for Implementing Back-Propagation Networks on FPGA: A Case Study -- FPNA: Concepts and Properties -- FPNA: Applications and Implementations -- Back-Propagation Algorithm Achieving 5 Gops on the Virtex-E -- FPGA Implementation of Very Large Associative Memories -- FPGA Implementations of Neocognitrons -- Self Organizing Feature Map for Color Quantization on FPGA -- Implementation of Self-Organizing Feature Maps in Reconfigurable Hardware -- FPGA Implementation of a Fully and Partially Connected MLP -- FPGA Implementation of Non-Linear Predictors -- The REMAP Reconfigurable Architecture: A Retrospective.
520			_aThe 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.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aRajapakse, Jagath C. _eeditor. _9302998
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387284859
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/0-387-28487-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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