The field of edge AI is rapidly evolving, with a focus on optimizing model performance, reducing latency, and improving energy efficiency. Researchers are exploring innovative approaches to deploy large language models and computer vision applications on edge devices, leveraging techniques such as scenario-aware routing, parallel inference, and embedded GPUs. These advancements aim to enable real-time processing, enhance user experience, and expand the adoption of edge AI in various domains, including mobile robotics and mechatronic systems. Noteworthy papers include ECVL-ROUTER, which introduces a scenario-aware routing framework for vision-language models, and EdgeReasoning, which characterizes the deployment of reasoning large language models on edge GPUs. EPARA is also notable for its end-to-end AI parallel inference framework in edge clouds, achieving higher goodput in production workloads compared to prior frameworks.
Edge AI Advancements
Sources
ECVL-ROUTER: Scenario-Aware Routing for Vision-Language Models
EPARA: Parallelizing Categorized AI Inference in Edge Clouds
Boosting performance of computer vision applications through embedded GPUs on the edge
EdgeReasoning: Characterizing Reasoning LLM Deployment on Edge GPUs
From the Laboratory to Real-World Application: Evaluating Zero-Shot Scene Interpretation on Edge Devices for Mobile Robotics
Keeping it Local, Tiny and Real: Automated Report Generation on Edge Computing Devices for Mechatronic-Based Cognitive Systems