Title: NeuralEQ: Neural-Network-Based Equalizer for High-Speed Wireline Communication. Abstract: Rapid growth of ML applications demand high-performance computing systems to perform massive data processing. In such systems, I/O bandwidth must be scaled up to prevent any performance degradation due to the limited data transfer rates. To meet this demand, recently wireline communication started adopting PAM4 signaling and DSP-based equalizers. However, multi-level signaling and conventional equalizing techniques degrade the bit-error-rate (BER) performance significantly. To mitigate this problem, this paper proposes a novel neural network architecture that mimics the forward-backward algorithm estimating the posterior probabilities in Hidden Markov Models. The proposed neural network overcomes the existing equalizer performance such as feed-forward equalizers or decision-feedback equalizers, while reducing the complexity of the forward-backward algorithm.