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аналитика - Deep Learning Compression
Post-Training Intra-Layer Multi-Precision Quantization for Efficient Deep Neural Network Deployment on Resource-Constrained Edge Devices
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.
DyCE: Dynamic Configurable Exiting for Deep Learning Compression and Scaling
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 introduces a dynamic configurable early-exit framework for deep learning compression and scaling, allowing real-time adaptation to varying performance-complexity requirements.
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