| dc.creator |
Espejo-Meana, S. |
|
| dc.creator |
Domínguez-Castro, R. |
|
| dc.creator |
Carmona-Galán, R. |
|
| dc.creator |
Rodríguez-Vázquez, Ángel |
|
| dc.date |
2008-03-30T18:54:30Z |
|
| dc.date |
2008-03-30T18:54:30Z |
|
| dc.date |
1994-09 |
|
| dc.date.accessioned |
2017-01-31T01:01:20Z |
|
| dc.date.available |
2017-01-31T01:01:20Z |
|
| dc.identifier |
Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems (MICRONEURO’94), pp. 383-391, Turin, Italy, September 1994. |
|
| dc.identifier |
http://hdl.handle.net/10261/3369 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3369 |
|
| dc.description |
This paper presents a continuous-time Cellular Neural Network (CNN) chip [1] for the application of Connected Component Detection (CCDet) [2]. Projection direction can be selected among four different
possibilities. Every cell (or pixel) in the 32 x 32 array includes a photosensor circuitry and an automatic tuning circuitry to adapt to average environmental illumination. Electrical image uploading is possible as well. Input pixel-values are stored on local memories (one per cell), allowing sequential processing of the acquired image in different directions. |
|
| dc.description |
The prototype has been designed and fabricated on a standard digital CMOS technology: 1.6μm, n-well, single-poly, double-metal. Circuit implementation is based on current-mode techniques and uses a systematic approach valid for any CNN application [3]. Cell dimensions, including the CNN processing circuitry, the photosensor and the adaptive circuitry are 145 x 150 μm2, of which the sensor and adaptive circuitry amounts to ~15% of the total pixel area and the wiring and multiplexing (required for direction selectability) to about 40%. The remaining 45% corresponds to the CNN processing circuitry. Pixel density is ~46 cells/mm2, and power dissipation is 0.33mW/cell. These area and power figures forecast single-die CMOS chips with 100 x 100 complexity and about 3W power consumption. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
172669 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Institute of Electrical and Electronics Engineers |
|
| dc.rights |
openAccess |
|
| dc.title |
A countinuous-time cellular neural network chip for direction-selectable connected component detection with optical image acquisition |
|
| dc.type |
Comunicación de congreso |
|