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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.
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Subject categories
- Academic > Computer Science > Computers - special aspects > Computer Brands > IBM 1130 ; Programming
- Academic > Mathematics > General > Mathematics
- Academic > Computer Science > Computer science
- Academic > Computer Science > Electronic data processing
- Academic > Computer Science > Neural computers
- Academic > Computer Science > Neural networks
- Academic > Mathematics > Instruments and machines
- Academic > Mathematics > Geometry. Trigonometry.Topology
- Computers > Networking
ISBNs
0387284877
9780387284859
9780387284873