Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks - Lin, Chunxiao; Azmine, Muhammad Farhan; Liang, Yibin; Yi, Yang (Frontiers in Computational Neuroscience, 2024)
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 realworld practicality. Neuroscience-inspired models such as echo state networks (ESN) present an appealing option by providing high accuracy and computational efficiency, thereby aligning well with the demanding criteria of 6G.In this work, We apply the ESN model 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 experiment results show the great performance and scalability of our design across a range of MIMO configurations (2×2, 4×4, 4×16, 4×64), compared with traditional MIMO symbol detection methods like linear minimum mean square error (LMMSE). We also validate the design through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. Our findings confirm the performance and feasibility of our system, reflected in a bit error rate results comparable to simulation outcomes.