FPGA implementation of a pulse density neural network with learning ability using simultaneous perturbation
FPGA implementation of a pulse density neural network with learning ability using simultaneous perturbation
复制标题
使用同时扰动的 FPGA 实现具有学习能力的脉冲密度神经网络
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Toshiki Tada
中科院分区：
文献类型：
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作者：
Y. Maeda;Toshiki Tada
在考虑神经网络的广泛应用时，硬件实现是非常重要的。特别是，具有学习能力的硬件神经网络非常吸引人。在这些网络中，学习方案是非常有趣的，反向传播方法被广泛使用。梯度型学习规则在电子系统中是不容易实现的，因为计算网络中所有权值的梯度是非常困难的。同时摄动法更合适，因为与反向传播法不同，学习规则只需要网络的前向操作来修改权值。此外，脉冲密度神经网络系统具有一些很有前途的特性，因为它们对噪声情况具有鲁棒性，并且可以处理基于数字电路的模拟量。本文描述了一种采用同步摄动方法作为学习方案的脉冲密度神经网络的现场可编程门阵列实现。通过算例验证了设计的可行性和实际神经网络系统的运行。
Hardware realization is very important when considering wider applications of neural networks (NNs). In particular, hardware NNs with a learning ability are intriguing. In these networks, the learning scheme is of much interest, with the backpropagation method being widely used. A gradient type of learning rule is not easy to realize in an electronic system, since calculation of the gradients for all weights in the network is very difficult. More suitable is the simultaneous perturbation method, since the learning rule requires only forward operations of the network to modify weights unlike the backpropagation method. In addition, pulse density NN systems have some promising properties, as they are robust to noisy situations and can handle analog quantities based on the digital circuits. We describe a field-programmable gate array realization of a pulse density NN using the simultaneous perturbation method as the learning scheme. We confirm the viability of the design and the operation of the actual NN system through some examples.