Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks
The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2024.1345644/full |
| _version_ | 1797301069875773440 |
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| author | Chunxiao Lin Muhammad Farhan Azmine Yibin Liang Yang Yi |
| author_facet | Chunxiao Lin Muhammad Farhan Azmine Yibin Liang Yang Yi |
| author_sort | Chunxiao Lin |
| collection | DOAJ |
| description | The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput. |
| first_indexed | 2024-03-07T23:16:04Z |
| format | Article |
| id | doaj.art-8b5083a6af674c0ebd3be85f41df9392 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-03-07T23:16:04Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-8b5083a6af674c0ebd3be85f41df93922024-02-21T11:26:21ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882024-02-011810.3389/fncom.2024.13456441345644Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networksChunxiao LinMuhammad Farhan AzmineYibin LiangYang YiThe field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.https://www.frontiersin.org/articles/10.3389/fncom.2024.1345644/fullecho state network6Gmassive MIMOOFDMAIFPGA |
| spellingShingle | Chunxiao Lin Muhammad Farhan Azmine Yibin Liang Yang Yi Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks Frontiers in Computational Neuroscience echo state network 6G massive MIMO OFDM AI FPGA |
| title | Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks |
| title_full | Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks |
| title_fullStr | Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks |
| title_full_unstemmed | Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks |
| title_short | Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks |
| title_sort | leveraging neuro inspired ai accelerator for high speed computing in 6g networks |
| topic | echo state network 6G massive MIMO OFDM AI FPGA |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2024.1345644/full |
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