# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Quantization-Based Real-Time Super Resolution
#### 3.5.1 Quantized CNN-Based Approaches  
[PERSON] [17] presents XLSR, a unique, hardware-aware model built for real-time SR with an emphasis on extreme lightweight and robustness to quantization--specifically with the Synaptics Dolphin NPU in mind. The root modules established in the DeepRoots framework for image categorization served as inspiration for the model's fundamental design. The author successfully customized these root modules to tackle the SISR issue. To improve the model's resilience to uint8 quantization, they added a Clipped ReLU function to the network's last layer, resulting in an ideal combination of image reconstruction quality and processing time. Interestingly, despite having 30 times fewer parameters than the VDSR model, XLSR surpassed VDSR on the Div2K validation dataset. In [83], the authors suggest a novel approach for SR networks known as Content-Aware Dynamic Quantization (CADyQ), which aims to reduce large bits while minimizing accuracy loss. CADyQ dynamically assigns optimum bit widths to different local areas and network layers based on the particular information in the input image. A trainable bit selection module is presented to identify the optimal bit width and quantization level for each layer and local picture patch. This module is based on quantization sensitivity, which is computed by combining the average magnitude of the patch's image gradient alongside the standard deviation of the layer's input features.  
Furthermore, [PERSON] et al. [45] introduce a basic convolutional network called NCNet, which includes a quick nearest-convolution module. This architecture is NPU-friendly, allowing for dependable real-time SR. Their technique uses the closest convolution, which provides the same performance as nearest upsampling but is significantly quicker and more suited to Android NNAPI. The model is easily deployable on mobile devices that support 8-bit quantization and is completely interoperable with all popular mobile AI accelerators. They also conducted extensive experiments on mobile devices using various tensor operations to demonstrate the effectiveness of their network design in real time.  
Discussion and Limitations  
The fast development of DNNs, particularly CNNs, has resulted in substantial advances in a variety of domains. However, the computational needs and huge parameter values of these models have restricted their use on resource-constrained devices such as mobile phones. To overcome this, numerous approaches have arisen to make CNNs more efficient while maintaining performance. The XLSR model, for example, has been created with hardware awareness in mind, allowing it to be used on specialized NPUs such as the Synaptic Dolphin. It includes a Clipped ReLU function, which improves resilience to quantization and achieves excellent performance with many fewer parameters. Similarly, CADyQ introduces dynamic quantization that optimally adjusts bit widths across network layers and local regions based on image content, further reducing computational load while preserving accuracy. NCNet presents an NPU-friendly architecture tailored for real-time SR on mobile devices. Despite these advancements, limitations remain. The dependence on specific hardware accelerators like NPUs may restrict wider deployment across different platforms. Moreover, dynamic quantization techniques, while effective, add complexity to the training process and may lead to performance trade-offs, especially in highly detailed tasks where extreme quantization can degrade accuracy.