This paper systematically sorts out the technical connotation and typical cases of lightweight models, and analyzes their application practices in the fields of medical image analysis, financial real-time risk control, and predictive maintenance of industrial Internet of Things.
Experiments on various edge devices and DNN models, including CNN-based and transformer-based workloads, show that ProfEdge reduces profiling errors by up to 80% and saves over 70% of profiling construction cost compared with existing methods.
Wei-Long Wang, Song-Tao Lu, Jia-Wei Liu et al.· ACM Transactions on Internet...· 0 citations
Findings confirm that combining complementary compression strategies yields substantially better performance-efficiency trade-offs than any single technique applied in isolation.
Upma Sharma Archana· International Journal of Res...· 0 citations
This article points out that model selection needs to be closely integrated with the constraints of specific application scenarios, and observes that future breakthroughs may lie in exploring the lightweighting of hybrid architectures that combine convolution and Transformer.
Experimental results demonstrate that the proposed framework significantly reduces model size and inference time compared to standard approaches, making it suitable for real-time applications in remote or low-resource clinical settings.
Amitava Biswas· Journal of Machine Learning...· 0 citations
This work surveys dozens of recent works that report compression results on real hardware and extracts practical deployment guidelines from them, and deploys compact language and image models on GPU, CPU, and Raspberry Pi platforms across question answering and image segmentation.
Subhransu Das, Jiaming Cheng, Arnav Kumar et al.· 0 citations
Results indicate that a teacher’s fine-tuned quality, rather than domain-specific pretraining, predicts distillation success, and offer practical guidance for edge deployment of medical image classifiers.
Shu-Wei Liu· Informatica· 0 citations
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