Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Array
Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Array
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使用现场可编程门阵列的人工神经网络的硬件实现
DOI：
10.7763/ijcte.2013.v5.795
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Haitham K. Ali
中科院分区：
文献类型：
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作者：
Esraa Zeki Mohammed;Haitham K. Ali
在本文中，介绍了在现场可编程门阵列（FPGA）上的人工神经网络的硬件实现。数字系统体系结构旨在实现馈电多层神经网络。使用非常高速集成电路硬件说明语言（VHDL）描述了设计的体系结构。神经网络的并行结构使其可能快速计算某些任务。相同的功能使神经网络非常适合在VLSI技术中实现。神经网络（NN）的硬件实现在很大程度上取决于单个神经元的有效实现。基于FPGA的可重构计算体系结构适用于神经网络的硬件实现。 FPGA意识到具有大量神经元的ANN仍然是一项具有挑战性的任务。关键字 - 组件；人工神经网络，硬件说明语言，现场可编程栅极阵列（FPGA），Sigmoid激活功能。
In this paper a hardware implementation of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture is designed to realize a feedforward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL). The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. Hardware realization of a Neural Network (NN), to a large extent depends on the efficient implementation of a single neuron. FPGA-based reconfigurable computing architectures are suitable for hardware implementation of neural networks. FPGA realization of ANNs with a large number of neurons is still a challenging task. Keywords-component; Artificial Neural Network, Hardware Description Language, Field Programmable Gate Arrays (FPGAs), Sigmoid Activation Function.