Purpose

The purpose of this paper is to develop an field programmable gate array (FPGA)-based neuron circuit to mimic dynamical behaviors of tabu learning neuron model.

Design/methodology/approach

Numerical investigations for the tabu learning neuron model show the coexisting behaviors of bi-stability. To reproduce the numerical results by hardware experiments, a digitally FPGA-based neuron circuit is constructed by pure floating-point operations to guarantee high computational accuracy. Based on the common floating-point operators provided by Xilinx Vivado software, the specific functions used in the neuron model are designed in hardware description language programs. Thus, by using the fourth-order Runge-Kutta algorithm and loading the specific functions orderly, the tabu learning neuron model is implemented on the Xilinx FPGA board.

Findings

With the variation of the activation gradient, the initial-related coexisting attractors with bi-stability are found in the tabu learning neuron model, which are experimentally demonstrated by a digitally FPGA-based neuron circuit.

Originality/value

Without any piecewise linear approximations, a digitally FPGA-based neuron circuit is implemented using pure floating-point operations, from which the initial conditions-related coexisting behaviors are experimentally demonstrated in the tabu learning neuron model.

中文翻译：

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基于 FPGA 的实验，用于证明禁忌学习神经元模型中的双稳定性

目的

本文的目的是开发一种基于现场可编程门阵列 (FPGA) 的神经元电路，以模拟禁忌学习神经元模型的动态行为。

设计/方法/方法

禁忌学习神经元模型的数值研究显示了双稳态的共存行为。为了通过硬件实验重现数值结果，通过纯浮点运算构建基于数字 FPGA 的神经元电路，以保证较高的计算精度。基于 Xilinx Vivado 软件提供的常用浮点运算符，神经元模型中使用的具体函数设计在硬件描述语言程序中。因此，通过使用四阶 Runge-Kutta 算法并有序加载特定函数，在 Xilinx FPGA 板上实现了禁忌学习神经元模型。

发现

随着激活梯度的变化，在禁忌学习神经元模型中发现了与初始相关的双稳态共存吸引子，这通过基于数字FPGA的神经元电路进行了实验证明。

原创性/价值

在没有任何分段线性近似的情况下，使用纯浮点运算实现了基于数字 FPGA 的神经元电路，由此在禁忌学习神经元模型中实验证明了与初始条件相关的共存行为。