Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
Integration of deep learning into the iot: A survey of techniques and challenges for real-world applications
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A Comparative Study of CNN Optimization Methods for Edge AI: Exploring the Role of Early Exits
Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.