Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks

Area of Science:

• Wireless Communication
• Machine Learning
• Neuroscience-Inspired Computing

Background:

• 6G technology demands higher data rates and processing speeds.
• Energy efficiency is crucial for practical 6G implementation.
• Massive MIMO-OFDM systems are key enablers for 6G.

Purpose of the Study:

• To apply echo state networks (ESNs) for efficient symbol detection in massive MIMO-OFDM systems.
• To design and validate a hardware-accelerated ESN architecture for symbol detection.
• To evaluate the performance and feasibility of the ESN-based system in real-world scenarios.

Main Methods:

• Utilized a neuroscience-inspired machine learning model: echo state network (ESN).
• Developed a hardware-accelerated reservoir neuron architecture for ESN implementation.
• Validated the design on a Xilinx Virtex-7 FPGA board.

Main Results:

• The ESN-based symbol detector demonstrated superior performance and scalability compared to traditional methods (e.g., linear MMSE).
• Achieved low bit error rates, indicating high detection accuracy.
• Showcased low resource utilization and high throughput on the FPGA.

Conclusions:

• The proposed hardware-accelerated ESN is a feasible and high-performing solution for symbol detection in 6G massive MIMO-OFDM systems.
• Neuroscience-inspired models offer a promising approach for advancing wireless communication technologies.
• The system design validates the practical applicability of ESNs in demanding communication environments.