A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits
Deploying deep neural networks on edge devices requires balancing accuracy, latency, and resource constraints under realistic execution conditions. To fit models within these constraints, two broad strategies have emerged: static compression techniques such as pruning and quantization, which permanently reduce model size, and dynamic approaches such as early-exit mechanisms, which adapt computational cost at runtime. While both families are widely studied in isolation, they are rarely compared under identical conditions on physical hardware. This paper presents a unified deployment-oriented comparison of static compression and dynamic early-exit mechanisms, evaluated on real edge devices ...
| Authors | Nekane Fernandez, Ivan Valdes, Steven Van Vaerenbergh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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