weight quantization
Weight quantization refers to the process of reducing the precision of weights in a neural network model. It involves representing the weights using a limited number of bits instead of using full precision floating-point numbers. This technique helps to reduce the memory footprint and computational complexity of the model, making it more efficient for deployment on resource-constrained devices. By quantizing the weights, the model can achieve a good balance between accuracy and efficiency.
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Related Concepts (1)
Similar Concepts
- activation quantization
- canonical quantization
- deformation quantization
- integer quantization
- pruning and quantization
- quantization error
- quantization methods
- quantization noise
- quantization-aware training
- signal quantization
- weight decay
- weight discrimination
- weight initialization
- weight management
- weight update