Cellular automata machines: a new environment for modeling
Cellular automata machines: a new environment for modeling
CNNUC3: A Mixed-Signal 64 x 64 CNN Universal Chip
MICRONEURO '99 Proceedings of the 7th International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems
A neuron-MOS-based VLSI implementation of pulse-coupled neural networks for image feature generation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
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The design of a large-neighborhood cellular nonlinear network (LN-CNN) with propagating connections is proposed. The propagating connections are utilized to achieve large-neighborhood templates in the shape of diamonds. Based on the propagating connections, each LN-CNN cell can only be connected to neighboring cells without interconnections to farther cells. Thus, it is suitable for very large scale integration implementation. The LN-CNN functions of diffusion, deblurring, and Müller-Lyer illusion are successfully verified. Meanwhile, the functions of erosion and dilation are expanded with the diamond-shaped LN templates. Furthermore, the simple N- and P-type synapses stop all the static current paths so that the dc power dissipation can be reduced to only 0.7 mW on standby and 18 mW in operation. An experimental LN-CNN chip with a 20 × 20 array has been fabricated using 0.18- µm CMOS technology. With the proposed LN-CNN chip, more applications and LN-CNN templates can be studied further.