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Extreme Quantization
Precision reduction technique pushing model parameters down to 1-2 bits for maximum compression, partially sacrificing accuracy for efficiency.
Binary Quantization
Quantization method where each weight and activation is represented by a single bit (-1 or +1), drastically reducing memory and accelerating computations.
Ternary Quantization
Technique using three values typically (-1, 0, +1) to represent weights, offering a better trade-off between compression and performance than pure binarization.
1-bit Quantization
Extreme form of quantization where each model parameter is stored on a single bit, enabling a 32x reduction compared to standard 32-bit models.
2-bit Quantization
Representation of weights and activations on two bits, allowing four quantization levels (-3, -1, +1, +3) with a better accuracy/efficiency balance.
Weight Binarization
Process of converting neural network weights into binary values while preserving activations at higher precision to maintain performance.
Post-Training Extreme Quantization
Technique applied after training to reduce parameter precision to 1-2 bits without requiring complete model retraining.
Extreme Quantization-Aware Quantization
Advanced method taking into account the impact of extreme quantization during the calibration process to minimize performance degradation.
Extreme Quantization with Extreme Learning
Approach where the model is fine-tuned specifically to adapt to extreme quantization constraints, better preserving final accuracy.
Binary Neural Network
Architecture where weights and activations are fully binarized, using XNOR and popcount operations for ultra-optimized computations.
Ternary Neural Network
Variant of binary networks using three states, allowing better expressivity while maintaining strong compression and computational efficiency.
Extreme Asymmetric Quantization
1-2 bit quantization method using asymmetric value ranges to better adapt to non-centered weight distributions.
Extreme Symmetric Quantization
Quantization approach where the value range is centered on zero, simplifying calculations but potentially less effective for certain distributions.
Model Compression via Extreme Quantization
Global technique combining extreme quantization with other compression methods to achieve compression rates exceeding 100x.
Minimal Precision Optimization
Process aiming to determine the minimum bit precision required for each layer of the model while maintaining acceptable performance levels.
Calibration for Extreme Quantization
Critical phase where quantization parameters are optimized using a small dataset to minimize the impact of extreme precision reduction.
Quantification Adaptative Extrême
Technique ajustant dynamiquement le niveau de quantification (1 ou 2 bits) par couche ou par neurone en fonction de leur sensibilité à la réduction de précision.
Stabilité de la Quantification Extrême
Propriété mesurant la robustesse d'un modèle face à la quantification extrême, essentielle pour garantir des performances fiables en déploiement.