
The proposed Post-Training Intra-Layer Multi-Precision Quantization (PTILMPQ) method effectively reduces the memory footprint of deep neural networks while preserving model accuracy, enabling efficient deployment on resource-constrained edge devices.


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post-training-intra-layer-multi-precision-quantization-for-efficient-deep-neural-network-deployment-on-resource-constrained-edge-devices


Post-Training Intra-Layer Multi-Precision Quantization for Efficient Deep Neural Network Deployment on Resource-Constrained Edge Devices


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DyCE introduces a dynamic configurable early-exit framework for efficient deep learning model compression and scaling.


dyce-dynamic-configurable-exiting-for-deep-learning-compression-and-scaling


DyCE: Dynamic Configurable Exiting for Deep Learning Compression and Scaling



DyCE introduces a dynamic configurable early-exit framework for deep learning compression and scaling, allowing real-time adaptation to varying performance-complexity requirements.
