Optimizing computational complexity (CC) is key to deploying efficient NN equalizers on resource-constrained hardware. To achieve this, we focus on three key areas: (i) training, (ii) inference, and (iii) hardware synthesis.
🔹 Training Efficiency
Reducing CC in training leads to simpler models with fewer parameters, faster convergence, and better generalization. Key techniques include:
✅ Transfer Learning (TL) – Fine-tuning pre-trained models for efficient training on limited data.
✅ Domain Randomization – Generating synthetic data for improved robustness.
✅ Semi-Supervised Learning – Enhancing performance with fewer labeled samples.
✅ Meta Learning & Multi-Task Learning (MTL) – Enabling quick adaptation to new tasks with shared learning.
✅ Data Augmentation & Dimensionality Reduction – Reducing dataset size while maintaining accuracy and minimizing overfitting.
💡 Why it matters
By leveraging these strategies, we can accelerate NN equalizer training, reduce hardware requirements, and improve performance—critical for next-gen optical communication and signal processing applications.
Would love to hear your thoughts! How do you approach complexity reduction in ML models? Let’s discuss in the comments! 👇
#MachineLearning #NeuralNetworks #ComplexityReduction #AI #SignalProcessing #OpticalCommunications
