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Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems.
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Connecting multiple and heterogeneous hardware devices to solve problems raises some challenges, especially in terms of interoperability and communications management.
In this paper, we present a method based on fault-tolerant hardware structure to solve the problem of instability inhere in routers.
Embedded systems typically use a microprocessor, combined with other hardware and software, to solve a specific computing problem.
Around a year ago, he started thinking about a project that would mix hardware and software to solve a problem that is dear to his heart — long-distance family communication.
We demonstrated that a combination of state of the art numerical methods, the utilization of modern hardware, and a hardware optimized implementation allows to solve this large-scale nonlinear inverse problem within a few seconds on standard desktop PCs, without compromising accuracy.
In this paper we demonstrate that a combination of state of the art numerical algorithms and a careful hardware optimized implementation allows to solve this large-scale inverse problem in a few seconds on standard desktop PCs with modern graphics hardware.
So corporations increasingly want to buy packages of hardware, software and services to solve business problems, like streamlining procurement or tracking customer behavior, instead of buying computer products à la carte and then trying to figure them out.
We use approximations to solve hardware limitations.
This suite seeks to solve hardware computational problems through software.
That two-faced legacy is one this generation of hardware doesn't begin to solve.
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