چکیده مقاله
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
کلیدواژهها
نویسندگان
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
شیوه ارجاع
Ghiasi, Ahmad and Abbasi, Reza and Ramezani, Molood and Kiani, Kosar,1403,Optimized Implement at ion of the Hodgkin-Huxley Model on FPGA Using 𝟐𝟐 Modules and Taylor Series Expansion for Enhanced Speed and Efficiency,The 2th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences.,Mashhad
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