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Structurally Constrained Quantization
Neural compression technique that applies specific quantization constraints while preserving the architecture and structural relationships of the neural network.
Quantization Aware Training (QAT)
Training method that simulates the effects of quantization during the learning process to minimize post-quantization accuracy loss.
Post-Training Quantization (PTQ)
Quantization process applied after the complete training of the model without requiring additional retraining.
Quantization Granularity
Level of detail at which parameters are grouped for quantization, ranging from tensor level to channel or group level.
Quantization Bias
Systematic error introduced when converting floating-point numbers to low-precision representation.
Quantization Calibration
Process of determining optimal quantization parameters (scale and zero-point) by analyzing the distribution of activations.
Weight Clustering
Technique grouping similar weights into clusters sharing a common representative value to reduce complexity.
Quantized Precision
Number of bits used to represent each quantized value, typically 8, 4, 2 or 1 bit in extreme methods.
Dynamic Range
Range of values that the quantized format can represent, crucial for preserving important information from the model.
Low-Precision Quantization
Extreme compression using less than 8 bits per parameter, requiring advanced techniques to maintain performance.
Weight Reconstruction
Optimization process aiming to minimize the error between original weights and their reconstructed quantized versions.
Quantization Structure Optimization
Algorithms automatically determining the optimal quantization configuration for each part of the network.
Quantization Sensitivity
Measure of the impact of quantization on the performance of a specific layer, guiding heterogeneous quantization strategies.
Quantization with Topology Constraints
Approach preserving the topological properties of the network while applying specific quantization constraints.