SUMMARYWe present here new cellular neural/non-linear networks (CNN)-based emulated digital architectures specifically designed for the solution of different partial differential equations (PDE). The array structure and local connectivity of the CNN paradigm make it a natural framework to describe the behaviour of locally interconnected dynamical systems. Solution of the PDE is carried out by a spatio-temporal dynamics, which can be computed in real-time on analogue CNN-UM chips, but the accuracy of the solution is low. Additionally, solution of PDEs on a CNN-UM architecture often requires a multi-layer structure and non-linear templates which is partially or not supported on the current analogue VLSI CNN-UM chips. To overcome these obstacles while preserving high computing performance a configurable emulated digital CNN-UM can be used where the main parameters (accuracy, template size and number of layers) are configurable. Additionally, the symmetry of the finite difference operators makes it possible to specialize the emulated digital CNN-UM architecture to solve a specific type of PDE, which results in higher performance. Emulated digital CNN-UM processors use fixed-point numbers to carry out computations, and by decreasing the precision the speed of the computations can be improved. Hence, a simple algorithm is introduced to determine the optimal fixed-point precision and maximize computing performance.

SUMMARYThe paper addresses the issue of implementing an embedded global analogic programming unit (GAPU) on the reconfigurable emulated-digital cellular neural/nonlinear networks universal machine (CNN-UM) architecture that has been extended by a flexible Xilinx MicroBlaze soft processor core to take full advantage of the joint computing power of high-speed distributed arithmetics and programmability. The implemented GAPU provides a stand-alone operation, which is capable of controlling complex sophisticated CNN analogic algorithms similar to various visual microprocessors, such as the ACE4k, ACE16k, and Bi-i vision systems. The quality of the embedded GAPU implementation is demonstrated by an analogic algorithm, in which sequences of template operations are required. Based on the experiments, several important issues relating to the acceleration efficiency, accuracy, cell size, and area consumption are discussed and compared with different CNN-UM implementations.

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The function of the low-level image processing that takes place in the biological retina is to compress only the relevant visual information to a manageable size. The behavior of the layers and different channels of the neuromorphic retina has been successfully modeled by cellular neural/nonlinear networks (CNNs). In this paper, we present an extended, application-specific emulated-digital CNN-universal machine (UM) architecture to compute the complex dynamic of this mammalian retina in video real time. The proposed emulated-digital implementation of multichannel retina model is compared to the previously developed models from three key aspects, which are processing speed, number of physical cells, and accuracy. Our primary aim was to build up a simple, real-time test environment with camera input and display output in order to mimic the behavior of retina model implementation on emulated digital CNN by using low-cost, moderate-sized field-programmable gate array (FPGA) architectures.