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.