View source for Ni1000

{{Short description|Artificial neural network chip}}
[[File:Intel-Nestor Ni1000.jpg|thumb|Add-in board with Intel/Nestor Ni1000]]
The '''Ni1000''' is an [[artificial neural network]] chip developed by [[Nestor Corporation]] and [[Intel]], developed in the 1990s. It is Intel's second-generation neural network chip, but the first all-digital chip. The chip is aimed at [[Image analysis|image analysis applications]]– containing more than 3 million transistors – and can analyze 40,000 patterns per second.<ref>{{cite magazine |last=Baran |first=Nicholas |date=March 1994 |title=Intel and Nestor to Commercialize Neural-Net Chip |archive-url=https://web.archive.org/web/19961221001221/http://www.byte.com/art/9403/sec3/art10.htm |magazine=Byte |location= |publisher= |url=http://www.byte.com/art/9403/sec3/art10.htm |access-date=21 October 2022 |archive-date=1996-12-21 }}</ref> Prototypes running Nestor's [[Optical character recognition|OCR]] software in 1994 were capable of recognizing around 100 handwritten characters per second. The development was funded with money from [[DARPA]] and [[Office of Naval Research]].<ref>{{Cite web |url=https://techmonitor.ai/technology/intels_ni1000_chip_holds_prospect_of_commercial_neural_networks|title=Intel's Ni1000 chip holds prospect of commercial neural networks |website=CBROnline archive at techmonitor.ai |date=21 February 1993 |access-date=2021-05-14 |df=dmy-all}}</ref>

==References==
{{Reflist}}
*[http://www.warthman.com/projects-Intel-Nestor-Ni1000-Recognition-Accelerator-Technical-Specification.htm Intel/Nestor Ni1000 Recognition Accelerator Technical Specification]
*{{cite book |isbn=9780262201049 |last1=Perrone |first1=Michael P. |last2=Cooper |first2=Leon N. |author-link2=Leon Cooper |editor-first1=Todd K. |editor-last1=Leen |editor-first2=Gerald |editor-last2=Tesauro |editor-first3=David S. |editor-last3=Touretzky |date=1995 |title=Advances in Neural Information Processing Systems 7 |chapter=The Ni1000: High Speed Parallel VLSI for Implementing Multilayer Perceptrons |url=https://proceedings.neurips.cc/paper/1994/file/430c3626b879b4005d41b8a46172e0c0-Paper.pdf |pages=747–754 |publisher=MIT Press |access-date=21 October 2022}}

[[Category:Artificial neural networks]]


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