Stochastic bitstream-based CNN and its implementation on FPGA

International Journal of Circuit Theory and Applications

Volumn 37, Issue 4, 2009, Pages 587-612

  Rák, Áadám ^a   Soós, Balázs Gergely ^a   Cserey, György ^a  

^a PÁZMÁNY PÉTER CATHOLIC UNIVERSITY   (Hungary)

Author keywords

Cnn (cellular nonlinear network); Fpga imple mentation; Parallel processing; Stochastic bitstream

Indexed keywords

BIT DEPTH; BIT STREAM; BITSTREAMS; CELL INTERACTION; CELLULAR NONLINEAR NETWORKS; CNN (CELLULAR NONLINEAR NETWORK); COMPUTATION TIME; DATA CARRIERS; FPGA IMPLE- MENTATION; FPGA IMPLEMENTATIONS; HARDWARE COMPLEXITY; HARDWARE IMPLEMENTATIONS; PARALLEL PROCESSING; SIMULATION RESULT; STOCHASTIC BITSTREAM; STOCHASTIC DATA; UNIVERSAL MACHINES;

COMPUTER HARDWARE DESCRIPTION LANGUAGES; FIELD PROGRAMMABLE GATE ARRAYS (FPGA); HARDWARE; NONLINEAR NETWORKS; PARALLEL ARCHITECTURES;

RANDOM PROCESSES;

EID: 67649207292     PISSN: 00989886     EISSN: 1097007X     Source Type: Journal    
DOI: 10.1002/cta.569     Document Type: Article

References (27)

1. Chua LO, Yang L. Cellular neural networks: theory and applications. IEEE Transactions on Circuits and Systems 1988; 35:1257-1290.
2. Chua LO, Roska T. The CNN paradigm. IEEE Transactions on Circuits and Systems 1993; 40:147-156.
3. Zarándy A, Rekeczky C, Szatmári I, Földesy P. The new framework of applications: the aladdin system. IEEE Journal on Circuits, Systems and Computers 2003; 12 (6) :764-781.
4. Eye-ris. http://www.anafocus.com.
5. Dudek P, Hicks P. A general-purpose processor-per-pixel analog simd vision chip. IEEE Transactions on Circuits and Systems-I: Analog and Digital Signal Processing 2005; 520 (1) :13-20.
6. Földesy P, Zarándy A, Rekeczky C, Roska T. Digital implementation of cellular sensor-computers. International Journal of Circuit Theory and Applications 2006; 34 (4) :409-428.
7. Zarandy A, Foldesy P, Szolgay P, Tokes S, Rekeczky C, Roska T. Various implementations of topographic, sensory, cellular wave computers. Proceedings of the IEEE International Symposium on Circuits and Systems, ISCAS 2005, Kobe, Japan, May 2005; 5802-5805.
8. Liñán G, Espejo S, Domínguez-Castro R, Rodríguez-Vázquez A. ACE4k: an analog I/O 64 × 64 visual microprocessor chip with 7-bit analog accuracy. International Journal of Circuit Theory and Applications 2002; 30 (2-3) :89-116.
9. Rodriguez-Vazquez A, Linan-Cembrano G, Carranza L, Roca-Moreno E, Carmona-Galan R, Jimenez-Garrido F, Dominguez-Castro R. Meana S. ACE16k: the third generation of mixed-signal SIMD-CNN ACE chips toward VSoCs. IEEE Transactions on Circuits and Systems Part I: Regular Papers 2004; 51 (5) :851-863.
10. Galan R, Garrido F, Castro R, Meana S, Vazquez A. CACE1K User's Guide, a Complex Analog and Logic Computing Engine with 1024 Processing Cells. Institute de Microelectronica de Sevilla CNM CSIC 2002; 23.
11. Zarandy A, Rekeczky C. Bi-i: a stand alone ultra high speed cellular vision system. IEEE Circuits and Systems Magazine 2005; 5 (2) :36-45.
12. Barr DRW, Carey SJ, Lopich A, Dudek P. A control system for a cellular processor array. Tenth International Workshop on Cellular Neural Networks and their Applications, 2006. CNNA '06, Istanbul, August 2006; 1-6.
13. Nagy Z, Vörösházi Z, Szolgay P. Emulated digital CNN-UM solution of partial differential equations: research articles. International Journal of Circuit Theory and Applications 2006; 34 (4) :445-470.
14. Nagy Z, Szolgay P. Configurable multilayer CNN-UM emulator on FPGA. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications [see also IEEE Transactions on Circuits and Systems I: Regular Papers] 2003; 50:774-778.
15. Keresztes P, Zarándy Á, Roska T, Szolgay P, Bezák T, Hidvégi T, Jónás P, Katona A. An emulated digital CNN implementation. The Journal of VLSI Signal Processing 1999; 23 (2) :291-303.
16. Ercsey-Ravasz M, Roska T, Néda Z. Stochastic simulations on the CNN universal machine. European Journal of Physics 2006; 51 (3) :407-412.
17. Hu J, Zhong S, Liang L. Exponential stability analysis of stochastic delayed cellular neural network. Journal of Chaos, Solitons and Fractals 2006; 27 (4) : 1006-1010.
18. Gaines BR. Stochastic computing systems. Advances in Information Systems Science 1969; 2:37-172.
19. Nguyen DD, Holt FB. Neural network using stochastic processing. United States Patent 4972363, 1990.
20. Daalen Mv, Kosel T, Jeavons P, Shawe-Taylor J. Emergent activation functions from a stochastic bit stream neuron. Electronics Letters 1994; 30 (4) :331-333.
21. van Daalen M, Jeavons P, Shawe-Taylor J. A stochastic neural architecture that exploits dynamically reconfigurable FPGAs. IEEE Workshop on FPGAs for Custom Computing Machines, Napa, CA, U. S. A., 1993; 202-211.
22. Bade SL. Hutchings BL. FPGA-based stochastic neural networks- implementation. IEEE Workshop on FPGAs for Custom Computing Machines Workshop, Napa Valley, CA, U. S. A., 1994; 189-198.
23. Kondo Y, Sawada Y. Functional abilities of a stochastic logic neural network. IEEE Transactions on Neural Networks 1992; 3:434-443.
24. Chua LO, Roska T. Cellular Neural Networks and Visual Computing, Foundations and Applications. Cambridge University Press: Cambridge, 2002.
25. Corinto F, Biey M, Gilli M. Non-linear coupled CNN models for multiscale image analysis. International Journal of Circuit Theory and Applications 2006; 34 (1) :77-88.
26. Corinto F, Gilli M. On stability of cellular neural networks with polynomial interactions. International Journal of Neural Systems 2003; 13 (6) :379-385.
27. Meador J, Wu A, Cole C, Nintunze N, Chintrakulchai P. Programmable impulse neural circuits. IEEE Transactions on Neural Networks 1991; 2:101-109.