Advanced Spike Signal Equalization via Long Short-Term Memory Neural Network

Abstract

Spike neural networks and neuromorphic chips and chiplets have recently drawn increasing attention. However, these systems utilize spike signals and address-event representation protocols, which differ fundamentally from traditional digital circuits. As a result, they may face different signal integrity issues and require special equalization methods. This article presents a long short-term memory (LSTM) based signal equalization approach that effectively addresses spike signal equalization of different data rates. This LSTM equalization method exhibits outstanding scalability in restoring spike signals at different data rates using one trained model. By implementing the LSTM equalization approach, high-precision signal equalization for spike signals is achieved, which demonstrates a remarkable advantage over conventional feed-forward equalization. These results verify the effectiveness of the proposed method for high-precision spike signal equalization and highlight its potential for neuromorphic and spike-based interconnect systems.