The Falcon emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture has been extended by an embedded GAPU (global analogic programming unit) using the flexible Xilinx MicroBlaze soft...
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The Falcon emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture has been extended by an embedded GAPU (global analogic programming unit) using the flexible Xilinx MicroBlaze soft-core processor 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 analogic algorithms, mainly in which sequences of template operations are required.
The paper addresses the issue of implementing a GAPU (global analogic programming unit) on the emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture. It has been embedded with a f...
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The paper addresses the issue of implementing a GAPU (global analogic programming unit) on the emulated-digital CNN-UM (cellular neural/nonlinear networks universal machine) architecture. It has been embedded with a flexible Xilinx MicroBlaze soft-core processor to take full advantage of the joint computing power of high-speed arithmetics and programmability. The GAPU implementation accelerates the processing performance of the Falcon architecture by decreasing the I/O communication and it also provides a stand-alone operation, which is capable of controlling complex sophisticated analogic CNN algorithms similar to various visual microprocessors, such as the ACE16k and the Q-Eye in the Bi-i, and eye-RIS cellular vision systems. Our primary aim was to implement an FPGA-based emulated-digital CNN-UM with embedded GAPU for computation intensive, real image-processing tasks.
The solutions of partial differential equations (PDEs) play a key role in today's real world simulations. Computational Fluid Dynamics (CFD) is an important part of this area, which involves the problem of gas or ...
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The solutions of partial differential equations (PDEs) play a key role in today's real world simulations. Computational Fluid Dynamics (CFD) is an important part of this area, which involves the problem of gas or fluid flow over different obstacles, e.g., air flow around vehicles, buildings, or the flow of water in the oceans. In engineering applications the temporal evolution of non-ideal, compressible fluids is quite often modeled by the system of Navier-Stokes equations. They are a coupled set of nonlinear hyperbolic partial differential equations and form a relatively simple, yet efficient model of compressible fluid dynamics. In the paper the implementation of a CFD on Body Fitted Mesh geometry on the Cell Broadband Engine is described. An arbitrary surface can be more easily simulated on body fitted mesh than on rectangular computation domain.
This paper presents an extended emulated digital retina model to compute two different retina channels in video real-time. The proposed emulated digital implementation of the two-channel retina model was compared to t...
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This paper presents an extended emulated digital retina model to compute two different retina channels in video real-time. The proposed emulated digital implementation of the two-channel retina model was compared to the previously developed single channel model from three different points of view: processing speed, number of physical cells and accuracy. A real-time test environment with camera input and display output is going to be set up to analyze the retina model implementation on emulated digital CNN (Cellular Neural/Nonlinear Network) model by using a 6M gate equivalent FPGA (Field Programmable Gate Array).
In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one...
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In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one of the most exciting areas is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion. In this paper a CNN-UM based solver of 2D inviscid, adiabatic, compressible fluids will be presented. The governing equations are solved by using first- and second-order numerical methods. Unfortunately the necessity the coupled multi-layered computational structure with non-linear, space-variant templates does not make it possible to utilize the huge computing power of the analog CNN-UM chips. To improve the performance of our solution emulated digital CNN-UM implemented on FPGA has been used. Properties of the implemented specialized architecture is examined in terms of area, speed and accuracy.
In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one...
详细信息
In the area of mechanical, aerospace, chemical and civil engineering the solution of partial differential equations (PDEs) has been one of the most important problems of mathematics for a long time. In this field, one of the most exciting areas is the simulation of fluid flow, which involves for example problems of air, sea and land vehicle motion.
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