A new architecture for digital stochastic pulse-mode neurons based on the voting circuit
A new architecture for digital stochastic pulse-mode neurons based on the voting circuit
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基于投票电路的数字随机脉冲模式神经元新架构
DOI：
10.1109/tnn.2005.852972
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发表时间：
2005
影响因子：
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通讯作者：
A. Abramo
中科院分区：
文献类型：
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作者：
Matteo Martincigh;A. Abramo
本文提出了一种新的基于表决电路的人工数字神经元结构，它可以被认为是文献中提出的结构的改进版本。已经使用了随机脉冲调制，其中神经元的输入值根据比特概率进行编码。由此产生的激活函数非常类似于logistic sigmoid，具有可以在架构级别选择的过渡斜率，而无需额外的硬件要求。所提出的神经元结构已在软件中进行了模拟。仿真结果证实，神经元具有类似于传统的表决电路的S形传输特性。从可重构平台上的实现获得的神经元的资源占用，已被估计为显着低于以前的实现。对神经元的行为进行了理论分析。
This paper presents a new kind of architecture for artificial digital neurons based on the voting circuit, which may be considered an improved version of those presented in literature. Stochastic pulse modulation has been used, where the values of the neuron's inputs are coded in terms of bit probabilities. The resulting activation function closely resembles the logistic sigmoid, with a transition slope that can be selected at the architectural level with no additional hardware requirements. The proposed neuron architecture has been simulated in software. Simulation results confirm that the neuron features a sigmoid transfer characteristic similar to that of conventional voting circuits. The resource occupation of the neuron, as obtained from implementation on reconfigurable platforms, has been estimated to be significantly lower than previous implementations. The theoretical analysis of the neuron's behavior is also presented.
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：
IEEE Trans. Neural Networks
影响因子：
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作者：
Y. Maeda;Toshiki Tada
通讯作者：
Toshiki Tada