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Optimized Implement at ion of the Hodgkin-Huxley Model on FPGA Using 𝟐𝟐 Modules and Taylor Series Expansion for Enhanced Speed and Efficiency

سال انتشار: 1403
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 108
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شناسه ملی سند علمی:

ICCPM02_009

تاریخ نمایه سازی: 7 شهریور 1403

چکیده مقاله Optimized Implement at ion of the Hodgkin-Huxley Model on FPGA Using 𝟐𝟐 Modules and Taylor Series Expansion for Enhanced Speed and Efficiency

The current research demonst rates that innovative approachesin hardware implement at ion can effectively and rapidly deploy complex neural models. We have thoroughly evaluated the Hodgkin-Huxl ey model , recognized as a comprehensive biological model comprising fournonli near differential equations with complex functions. Due to its complexity and implement at ion challenges, we have f ocused on developing a more straight forward model with optimal implement at ion capabilities. During the FPGA hardware implement at ion phase, we utilized the VHDL hardware description l anguage. To speed up implement at ion and enhance efficiency, simplified equation sand 2𝑥 modules were employed. We designed the structure of the seequations to include only basic arithmetic operations, logical shifts, and clear equations. This approach leads to the development of an optimized neural model that reduces hardware resource consumption and operates at high speed, making i t highly beneficial for various applications across different fields.

کلیدواژه های Optimized Implement at ion of the Hodgkin-Huxley Model on FPGA Using 𝟐𝟐 Modules and Taylor Series Expansion for Enhanced Speed and Efficiency:

Neuron Hodgki n-Huxl ey (HH) ، Dynami c Hodgki n-Huxl ey ، Fi el d-Programmabl e Gat e Array (FPGA) ، Modul es 2𝑥

نویسندگان مقاله Optimized Implement at ion of the Hodgkin-Huxley Model on FPGA Using 𝟐𝟐 Modules and Taylor Series Expansion for Enhanced Speed and Efficiency

Ahmad Ghiasi

Department of Electrical Engineering, Kermanshah University of Technology,Kermanshah, Iran

Reza Abbasi

Department of Electrical Engineering, Darol fonoon University, Qazvin, Iran

Molood Ramezani

Department of Electrical Engineering, Shahrood University of Technology,Shahrood, Iran

Kosar Kiani

Department of Electrical Engineering, Birjand University, Birjand, Iran