神经网络的FPGA实现

Sujatha Kumari, Sudarshan Patil Kulkarni, C. G. Sinchana
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引用次数: 0

摘要

—硬件采用现场可编程门阵列(FPGA)构建人工神经网络。设计了一个数字系统的结构来执行前馈多层神经网络。ANN和CNN是非常常用的架构。Verilog被用来描述设计的架构。对于某些任务的计算,神经网络的分布式架构结构使其具有潜在的效率。同样的特点使得神经网络适合应用于超大规模集成电路技术。对于神经网络的硬件来说,单个神经元必须被有效地实现。基于fpga的可编程计算机系统对于神经网络的硬件实现非常有用。
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FPGA Implementation of Neural Nets
— The field programmable gate array (FPGA) is used to build an artificial neural network in hardware. Architecture for a digital system is devised to execute a feed-forward multilayer neural network. ANN and CNN are very commonly used architectures. Verilog is utilized to describe the designed architecture. For the computation of certain tasks, a neural network’s distribut ed architecture structure makes it potentially efficient. The same features make neural nets suitable for application in VLSI technology. For the hardware of a neural network, a single neuron must be effectively implemented (NN). Reprogrammable computer systems based on FPGAs are useful for hardware implementations of neural networks.
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来源期刊
CiteScore
1.50
自引率
14.30%
发文量
0
审稿时长
12 weeks
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