A Survey of Model Compression Techniques for TinyML Applications
Ismail Lamaakal, Chaymae Yahyati, Ibrahim Ouahbi, Khalid El Makkaoui, Yassine Maleh · 2025
The convergence of embedded systems and artificial intelligence has led to the rapid growth of TinyML, which enables ML inference on highly resource-constrained devices. However, deploying modern DL models in these environments poses signif-icant challenges due to limited memory, processing power, and energy availability. Model compression techniques have emerged as essential tools to bridge this gap, allowing for the deployment of efficient, low-latency, and accurate models at the edge. This survey provides a comprehensive and structured review of key model compression strategies-including low-rank factorization, neural architecture search, pruning, quantization, knowledge distillation, and other emerging methods-highlighting their principles, mathematical formulations, and application relevance to TinyML. We also discuss the trade-offs and challenges as-sociated with these methods, such as accuracy loss, hardware-software co-design, and deployment constraints. Our goal is to equip researchers and practitioners with a practical reference that informs the design of lightweight AI solutions suitable for the next generation of intelligent edge devices.