This paper is published in Volume-5, Issue-3, 2019
Area
Electronics and Communication Engineering
Author
Km Manisha, Dr. A. K. Gautam
Org/Univ
S. D. College of Engineering and Technology, Muzaffarnagar, Uttar Pradesh, India
Keywords
: Artificial Neural Network (ANN), FPGA, Xilinx ISE software
Citations
IEEE
Km Manisha, Dr. A. K. Gautam. Artificial Neural Network architecture and hardware Chip Implementation using VHDL, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Km Manisha, Dr. A. K. Gautam (2019). Artificial Neural Network architecture and hardware Chip Implementation using VHDL. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.
MLA
Km Manisha, Dr. A. K. Gautam. "Artificial Neural Network architecture and hardware Chip Implementation using VHDL." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.
Km Manisha, Dr. A. K. Gautam. Artificial Neural Network architecture and hardware Chip Implementation using VHDL, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Km Manisha, Dr. A. K. Gautam (2019). Artificial Neural Network architecture and hardware Chip Implementation using VHDL. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.
MLA
Km Manisha, Dr. A. K. Gautam. "Artificial Neural Network architecture and hardware Chip Implementation using VHDL." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.
Abstract
Artificial neural networks are extended on the basis of brain structure. Like the brain, ANNs can recognize patterns, handle facts and figures and be trained. They are prepared by artificial neurons which employ the quintessence of genetic neurons. In the research work, we have considered the 8 inputs ANN signal which is multiplied with their corresponding weights. The hardware chip is designed to support the system functionality in Xilinx ISE 14.2 software. The designed chip is simulated with Modelsim 10.0 software for test cases. The designed chip is also synthesized on SPARTAN-3E FPGA using VHDL programming and device hardware and timing parameters are also analyzed for the functionality of the chip. Keywords: Artificial Neural Network (ANN), FPGA, Xilinx ISE software