FPGA Acceleration of a Real-time Neural Network Controller for Solar Inverter
Author:
Affiliation:
1. Texas A&M University-Kingsville,Department of Electrical Engineering and Computer Science,Kingsville,USA
2. North Carolina A&T State University,Department of Computer Science,Greensboro,USA
Funder
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9965569/9965624/09965651.pdf?arnumber=9965651
Reference16 articles.
1. FPGA Based Neural Networks for Characters Recognition
2. Deep neural network accelerator based on FPGA
3. Toward Multi-FPGA Acceleration of the Neural Networks
4. DSP Implementation of a Novel Recurrent Neural Network Controller into a TI Solar Microinverter
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1. Accelerating the neural network controller embedded implementation on FPGA with novel dropout techniques for a solar inverter;Pervasive and Mobile Computing;2024-11
2. 32-Bit Fixed and Floating-Point Hardware Implementation for Enhanced Inverter Control: Leveraging FPGA in Recurrent Neural Network Applications;IEEE Access;2024
3. Accelerating FPGA Implementation of Neural Network Controllers via 32-bit Fixed-Point Design for Real-Time Control;2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON);2023-10-12
4. A Novel Weight Dropout Approach to Accelerate the Neural Network Controller Embedded Implementation on FPGA for a Solar Inverter;2023 IEEE International Conference on Smart Computing (SMARTCOMP);2023-06
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