Friday 28 February 2025
The quest for efficient artificial intelligence has led researchers to explore new ways to train and deploy neural networks on edge devices, like smartphones and smart home appliances. A recent paper proposes a novel approach that tackles this challenge by introducing a multi-task learning framework, called Ladon.
Ladon is designed to work with limited computing resources, making it ideal for real-world applications where speed and power consumption are crucial. The system consists of three main components: an encoder-decoder architecture, a knowledge distillation module, and task-specific modules for object detection, semantic segmentation, and image classification.
The encoder-decoder architecture is responsible for compressing input data into a compact representation that can be transmitted efficiently over wireless networks. This process is achieved through a combination of convolutional neural networks (CNNs) and autoencoders. The knowledge distillation module then refines the compressed data by fine-tuning the model using a teacher-student learning approach.
The task-specific modules are where Ladon truly shines. By leveraging multi-task learning, these modules can be trained simultaneously on multiple tasks, such as object detection, semantic segmentation, and image classification. This approach not only improves accuracy but also reduces the overall computational requirements.
To evaluate Ladon’s performance, researchers conducted experiments using various edge devices, including Jetson Nano and Jetson NX Xavier. The results show that Ladon significantly outperforms traditional single-task learning approaches in terms of end-to-end latency, peak memory usage, and local GMAC (Giga Multiply-Accumulate Operation).
One notable aspect of Ladon is its ability to adapt to different network conditions. In a simulated LoRa wireless network with a data rate of 37.5 Kbps, Ladon still managed to achieve impressive results, demonstrating its robustness in challenging environments.
While Ladon is not the first attempt at multi-task learning on edge devices, it offers several advantages over existing solutions. Its modular design allows for easy integration with various tasks and networks, making it a versatile tool for developers and researchers alike.
As AI continues to play an increasingly important role in our daily lives, efficient deployment strategies like Ladon will become crucial for unlocking the full potential of these technologies. By reducing computational requirements and improving accuracy, Ladon has set a new standard for edge AI applications, paving the way for more widespread adoption and innovation.
Cite this article: “Ladon: A Novel Multi-Task Learning Framework for Efficient Edge AI”, The Science Archive, 2025.
Artificial Intelligence, Neural Networks, Multi-Task Learning, Edge Devices, Smartphones, Smart Home Appliances, Limited Computing Resources, Encoder-Decoder Architecture, Knowledge Distillation, Wireless Networks.
