DocumentCode
358269
Title
The CNNUC3: an analog I/O 64x64 CNN universal machine chip prototype with 7-bit analog accuracy
Author
Linán, G. ; Espejo, S. ; Domìnguez-Castro, R. ; Rodriguez-Vazquen, A.
Author_Institution
Inst. de Microelectron., CSIC, Sevilla, Spain
fYear
2000
fDate
2000
Firstpage
201
Lastpage
206
Abstract
This paper describes a full-custom mixed-signal chip which embeds distributed optical signal acquisition, digitally-programmable analog parallel processing, and distributed image memory (cache) on a common silicon substrate. This chip, designed in a 0.5 μm CMOS standard technology contains around 1000000 transistors, 80% of which operate in analog mode. Chip functional features are in accordance to the CNN Universal Machine paradigm. The chip is capable to complete complex spatio-temporal image processing tasks within short computation time and using a low power budget. The internal circuitry of the chip has been designed to operate in robust manner with >7-bit equivalent accuracy in the internal analog operations, which has been confirmed by experimental measurements. Hence, to all practical purposes, processing tasks completed by the chip have the same accuracy than those completed by digital processors preceded by 7-bit digital-to-analog converters for image digitalization
Keywords
CMOS integrated circuits; cellular neural nets; image processing; mixed analogue-digital integrated circuits; neural chips; parallel processing; CMOS; CNNUC3; analog parallel processing; cellular neural networks; distributed image memory; image processing; mixed-signal chip; neural chips; universal machine chip; CMOS technology; Cellular neural networks; Circuits; Image processing; Parallel processing; Robustness; Semiconductor device measurement; Signal processing; Silicon; Turing machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location
Catania
Print_ISBN
0-7803-6344-2
Type
conf
DOI
10.1109/CNNA.2000.876845
Filename
876845
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