Zeinulla ZHANABAEV, Yeldos KOZHAGULOV, Dauren ZHEXEBAY

FPGA implementations of scale-invariant models of neural networks

Integrated circuit implementations of new models of neural networks with scale-invariant properties are presented. The specifics of such models are necessary in analysis of discrete mappings containing fractional power. We suggest an algorithm for increasing the power of a physical value by using a field-programmable gate array (FPGA). Comparisons between FPGA implementations and numerical results are demonstrated.

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