pruning and quantization
Pruning and quantization are techniques used in machine learning to reduce the size and complexity of deep neural networks. Pruning involves removing unnecessary connections and weights from a neural network, resulting in a smaller and more efficient model. This is typically achieved by setting small weights to zero or removing entire neurons or layers based on their importance to the network's performance. Pruning helps eliminate redundancy and improve computational efficiency. Quantization, on the other hand, focuses on reducing the precision of network weights and activations. It involves representing these values with fewer bits, usually by mapping them to a smaller range or using fixed-point representations. Quantization reduces the memory footprint of neural networks and enables faster inference on hardware platforms with limited computational resources. Pruning and quantization can be used together to further compress and optimize deep neural networks. Pruning helps create a sparse network, and quantization reduces the precision of the remaining non-zero weights. This combined approach achieves significant model compression, making neural networks more suitable for deployment on devices with limited resources, such as mobile phones and embedded systems.
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