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.
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.
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.
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.
This work was supported by the grants from the National Natural Science Foundations of China under Grant Nos. 51777016, 61801054 and 61705021, and the Natural Science Foundations of Jiangsu Province, China, under Grant No. BK20191451.
Zhu, D., Hou, L., Chen, M. and Bao, B. (2021), "FPGA-based experiments for demonstrating bi-stability in tabu learning neuron model", Circuit World, Vol. 47 No. 2, pp. 194-205. https://doi.org/10.1108/CW-12-2019-0189
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