Combining pruning, quantization, and early exits in CNNs reduces inference latency and memory on real edge devices with minimal accuracy loss.
Efficient hardware implementation of cellular neural networks with incremental quantization and early exit,
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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.