Optimized Equalization in IM/DD Optical Communications Using Hardware-Friendly Neural Networks

The increasing demand for high-speed, low-cost data transmission in short-reach optical interconnects has cemented intensity-modulated direct-detection (IM/DD) optical systems as a practical solution. However, nonlinear impairments stemming from square-law detection under chromatic dispersion channels, as well as the nonideal optoelectronic components, pose significant challenges to signal integrity. To address these issues, neural network (NN)-based equalization techniques have garnered considerable attention due to their inherent capability to model complex channel behaviors. While these approaches offer performance improvements, their deployment is often constrained by computational burden and hardware resource consumption. This chapter focuses on recent advances in hardware-friendly NN designs for IM/DD equalization, highlighting effective complexity reduction strategies such as network pruning, low-precision quantization, and multi-task learning. We examine a range of NN architectures and evaluate their adaptability to low-power, low-latency implementations. Furthermore, this chapter discusses practical trade-offs between accuracy and computational cost, with insights into deployment in real-time systems. We outline future directions toward scalable and resource-efficient NN equalization, paving the way for intelligent digital signal processing in next-generation optical communication links.