Optoelectronic neural processor for smart vision applications

References

• Malamas, E. N., Petrakis, E. G. M., Zervakis, M., Petit, L. and Legat, J. A survey on industrial vision systems, applications and tools. Image Vision Comput., 2003, 21, 171–188.
   Web of Science ®Google Scholar
• Stojanovicw, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S. and Papadopoulos, G. Real-time vision-based system for textile fabric inspection. Real-time Imag., 2001, 7, 507–518.
   Web of Science ®Google Scholar
• Loinaz, M. and Ackland, B. Video cameras: CMOS technology provides on-chip processing. Sensor Rev., 1999, 19, 19–26.
   Google Scholar
• Komuro, T., Kagami, S. and Ishikawa, M. A. A dynamically reconfigurable SIMD processor for a vision chip. IEEE J. Solid-state Circ., 2004, 39, 265–268.
   Web of Science ®Google Scholar
• Philipp, R. M. and Etienne-Cummings, R. Single-chip stereo imager. Analog Integr. Circ. Signal Process., 2004, 39, 237–250.
   Web of Science ®Google Scholar
• Rüedi, P. F., Heim, P., Kaess, F., Grenet, E., Heitger, F., Burgi, P. Y., Gyger, S. and Nussbaum, P. A 128 × 128 pixel 120-dB dynamic-range vision-sensor chip for image contrast and orientation extraction. IEEE J. Solid-state Circ., 2003, 38, 2325–2333.
   Web of Science ®Google Scholar
• Yu, F. T. S. and Yang, X. Introduction to Optical Engineering, 1997, Chap. 11 (Cambridge University Press, Cambridge ) ISBN 0 521 57366 1.
   Google Scholar
• Kang, K., Powell, J., Snack, R., Garvin, C. and Trezza, A. Optical Image Correlation Using High-Speed Multiple Quantum Well Spatial Light Modulators. Proc. SPIE, Vol. 3715, 1999, pp. 97–107 (SPIE, Bellingham, WA).
   Google Scholar
• Rosen, J. and Li, Y. Recent progress in electro-optical three-dimensional correlators. In Smart Imaging Systems (Ed. B. Javidi), 2001, pp. 133–158 (SPIE, Bellingham, WA).
   Google Scholar
• Roth, M. W. Survey of neural networks technology for automatic target recognition. IEEE Trans. Neural Networks, 1990, 1, 28–42.
   PubMedGoogle Scholar
• Lippmann, R. P. An introduction to computing with neural nets. IEEE ASSP Mag., 1987, April, 4–22.
   Google Scholar
• Meireles, M. R. G., Almeida, P. E. M. and Simoes, M. G. A comprehensive review for industrial applicability of artificial neural networks. IEEE Trans. Ind. Electron., 2003, 50, 583–601.
   Web of Science ®Google Scholar
• Rodriguez-Vazquez, A., Liñán-Cembrano, G., Roca-Moreno, E., Carmona-Galán, R., Jiménez-Garrido, F., Domínguez-Castro, R. and Espejo-Meana, S. ACE16K: the third generation of mixed-signal CIMD-CNN ACE chips toward VSoCs. IEEE Trans. Circuits Syst. I, 2004, 51, 851–863.
   Google Scholar
• Christodoulou, C., Bugmann, G. and Clarkson, T. G. A spiking neuron model: applications and learning. Neural Networks, 2002, 15, 891–908.
   PubMed Web of Science ®Google Scholar
• Escalera, A., Armingol, J. M. and Mata, M. Traffic sign recognition and analysis for intelligent vehicles. Image Vision Comput., 2003, 21, 247–258.
   Web of Science ®Google Scholar
• Fang, C. Y., Chen, S. W. and Fuh, C. S. Automatic change detection of driving environments in a vision- based driver assistance system. IEEE Trans. Neural Networks, 2003, 14, 646–657.
   PubMed Web of Science ®Google Scholar
• Hammerstron, D. A VLSI architecture for highperformance, low cost, on-chip learning. Int. Joint Conf. on Neural Networks II, June 1990, San Diego, USA, pp. 537–544.
   Google Scholar
• Holler, M., Tam, S. S, Castro, H. and Benson, R. An electrically trainable artificial neural network (ETANN) with 10240 ‘floating gate’ synapses. Int. Joint Conf. on Neural Networks II, June 1989, Washington DC, USA, pp. 191–196.
   Google Scholar
• Lyndsey, C. and Lindblad, T. Review of hardware neural networks: a user perspective. Int. J. Neural Syst., 1995, 6, 215–224.
   Google Scholar
• Farhat, N. H., Psaltis, D., Prata, A. and Paek, E. Optical implementation of the Hopfield model. Appl. Opt., 1985, 24, 1469–1475.
   PubMed Web of Science ®Google Scholar
• Jutamulia, S. and Yu, F. T. S. Overview of hybrid optical neural networks. Opt. Laser Technol., 1996, 28, 59–72.
   Web of Science ®Google Scholar
• Caulfield, H. J., Kinser, J. and Rogers, S. K. Optical neural networks. Proc. IEEE, 1989, 77, 1573–1583.
   Web of Science ®Google Scholar
• Hendry, D. C., Duncan, A. A. and Lighttowler, N. IP core implementation of a self-organizing neural network, IEEE Trans. Neural Networks, 2003, 14, 1085–1096.
   PubMed Web of Science ®Google Scholar
• General Vision Inc. Zero Instruction Set Computer Technology Reference Manual, revision date September 2003.
   Google Scholar
• Pulnix® ZiCAM® data sheet, 2004.
   Google Scholar
• Yang, F. and Paindavoine, M. Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification. IEEE Trans. Neural Networks, 2003, 14, 1162–1175.
   PubMed Web of Science ®Google Scholar
• Miller, D. A. B. Rationale and challenges for optical interconnects to electronic chips. Proc. IEEE, 2000, 88, 728–749.
   Web of Science ®Google Scholar
• Lamela, H., Ruiz-Llata, M. and Warde, C. Optical broadcast interconnection neural network. Opt. Eng. Lett., 2003, 42, 2487–2488.
   Web of Science ®Google Scholar
• Carvajal, R. G., Ramirez-Angulo, J. and Martinez- Heredia, J. High-speed high-precision min/max circuits in CMOS technology. Electron. Lett., 2000, 36, 697–699.
   Web of Science ®Google Scholar
• Ruiz-Llata, M. and Lamera, H. Image classification system based on the optical broadcast neural network processor. Appl. Opt., 2005, 44, 2366–2376.
   PubMed Web of Science ®Google Scholar
• Chapinal, G., Bota, S. A., Moreno, M., Palacin, J. and Herms, A. A 128 × 128 CMOS image sensor with analog memory for synchronous image capture. IEEE Sensors J., 2002, 2, 120–127.
   Web of Science ®Google Scholar
• Ruiz-Llata, M., Cambre, D. M. and Lamela, H. Image classification with an optoelectronic hardware neural network. Proc. OSAV’2004, Int. Topical Meeting on Optical Sensing and Artificial Vision, Saint Petersburg, Russia, 18–21 October 2004, State University ITMO, Moscow, pp. 157–163.
   Google Scholar
• Van Keulen, E., Colak, S., Withagen, H. and Hegt, H. Neural network hardware performance criteria. IEEE Int. Conf. on Neural Networks, 1994, IEEE, Piscataway, NJ, pp. 1885–1888.
   Google Scholar
• Lamela, H., Ruiz-Llata, M., Cambre, D. M. and Warde, C. Fast prototype of the optical broadcast interconnection neural network architecture. In SPIE's 49th Annual Meeting (Optical Information Systems II) (Eds. B. Javidi and D. Psalti), Proc. SPIE, Vol. 5557, 2004, pp. 247–254 (SPIE, Bellingham, WA).
   Google Scholar
• Ruiz-Llata, M., Lamela, H. and Warde, C. Design of a compact neural network based on an optical broadcast architecture. Opt. Eng., 2005, 44, 055401–01.
   Web of Science ®Google Scholar