DNN-SNN Co-Learning for Sustainable Symbol Detection in 5 G Systems on Loihi Chip

Performing symbol detection for multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is challenging and resource-consuming. In this paper, we present a liquid state machine (LSM), a type of reservoir computing based on spiking neural networks (SNNs), to achieve energy-efficient and sustainable symbol detection on the Loihi chip for MIMO-OFDM systems. SNNs are more biological-plausible and energy-efficient than conventional deep neural networks (DNN) but have lower performance in terms of accuracy. To enhance the accuracy of SNNs, we propose a knowledge distillation training algorithm called DNN-SNN co-learning, which employs a bi-directional learning path between a DNN and an SNN. Specifically, the knowledge from the output and intermediate layer of the DNN is transferred to the SNN, and we exploit a decoder to convert the spikes in the intermediate layers of an SNN into real numbers to enable communication between the DNN and the SNN. Through the bi-directional learning path, the SNN can mimic the behavior of the DNN by learning the knowledge from the DNN. Conversely, the DNN can better adapt itself to the SNN by using the knowledge from the SNN. We introduce a new loss function to enable knowledge distillation on regression tasks. Our LSM is implemented on Intel's Loihi neuromorphic chip, a specialized hardware platform for SNN models. The experimental results on symbol detection in MIMO-OFDM systems demonstrate that our LSM on the Loihi chip is more precise than conventional symbol detection algorithms. Also, the model consumes approximately 6 times less energy per sample than other quantized DNN-based models with comparable accuracy.