Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
Optimized convolutional neural network at the IoT edge for image detection using pruning and quantization,
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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.