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Hardware implementation design of analog sorting neural network

机译:模拟排序神经网络的硬件实现设计

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Hardware implementation design in FPGA based reconfigurable computing architecture of analog neural network for parallel sorting is presented. The network has low computational and hardware implementation complexity. It is capable to process signals of any finite range, possesses signal order preserving property and does not require resetting and corresponding supervisory circuit that increases a speed of signal processing. A hardware implementation design is performed by using NI LabView Real-Time System. The hardware blocks are based on Altera FPGA Cyclone III and STM ARM32 Microcontroller Unit. Simulation results demonstrating the network performance are provided. According to simulation results, the network implemented in hardware demonstrates much higher speed of sorting comparatively to its software implementation.
机译:呈现了基于FPGA的FPGA中的硬件实现设计,用于并行分类的模拟神经网络的模拟神经网络的可重新配置架构。网络具有低计算和硬件实现复杂性。它能够处理任何有限范围的信号,具有保留性能的信号量级,并且不需要复位和相应的监控电路,这些电路增加了信号处理的速度。使用NI LabVIEW实时系统执行硬件实现设计。硬件块基于Altera FPGA Cyclone III和STM ARM32微控制器单元。提供了展示网络性能的仿真结果。根据仿真结果,硬件中实现的网络相比,在硬件中实现的速度更高,相对较高。

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