# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 4 Emerging Solutions and State-of-the-Art Approaches  
### Model Compression Techniques  
Model compression stands at the forefront of enabling AI in resource-constrained environments by reducing the memory footprint and computational demands of neural networks [28]. Among these techniques, quantization is a widely adopted method that converts floating-point weights and activations to lower bit representations--commonly 8-bit integers--to diminish the size of the model and accelerate inference [69]. Advanced quantization approaches push precision even lower, exploring 4-bit or 1-bit representations, though such aggressive strategies can degrade accuracy if not carefully calibrated and retrained [12]. Tools like TensorFlow Lite provide built-in utilities for post-training quantization, which can drastically cut the size of pre-trained models with minimal performance loss [70].  
Pruning and sparsity target the removal or zeroing out of redundant weights or channels within a network to reduce computation. Structured pruning, for instance, systematically eliminates entire filters in convolutional layers, leading to more regular sparsity patterns that can be more efficiently accelerated by specialized hardware [71]. Combined with quantization, pruning can reduce model size by an order of magnitude or more, making it feasible to deploy deep neural networks on microcontrollers with very limited memory resources. Knowledge distillation offers another powerful technique, in which a smaller \"student\" network learns to emulate the outputs of a larger \"teacher\" model, effectively capturing the teacher's learned representations while operating on a fraction of the parameters [72]. Often, these techniques are combined in a holistic approach to maximize memory savings, striking a fine balance between a model's resource footprint and inference accuracy [29].  
Among the various model compression techniques that are vital for enabling AI on constrained hardware, quantization and pruning represent two foundational approaches, often used in conjunction. While both aim to reduce model size and computational cost, they operate via distinct mechanisms and entail different trade-offs regarding accuracy, hardware compatibility, and implementation complexity. To clarify their respective strengths and weaknesses, Table 3 offers a critical analysis comparing these methods across several key dimensions. This includes their typical impact on memory footprint, suitability for hardware acceleration, potential effects on model accuracy, deployment complexity, and achievable real-world speedups, thereby highlighting the essential considerations involved in selecting and applying these techniques for TinyML applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimension** & **Quantization** & **Pruning** & **References** \\ \hline \multirow{3}{*}{Memory footprint} & High reduction (e.g., 4–8\(\times\)) by & Moderate–high reduction by & \multirow{3}{*}{[69; 70]} \\  & reducing numerical precision & removing entire & & \\  & & weights/channels & \\ \hline \multirow{3}{*}{Hardware acceleration} & Typically straightforward to & Dependent on kernel support for & \multirow{3}{*}{[71]} \\  & accelerate with integer ops & sparse operations & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Critical analysis: quantization vs. pruning: accuracy vs. speed trade-offs.