Neural Network Quantization via Adaptive Bit Allocation

The patent applies parameter changes by dynamically adjusting quantization parameters (precision levels) for different neural network components based on their statistical properties. Specifically, it computes mean and standard deviation for each layer's parameters and activations, then determines optimal quantization bit depths that balance quality and compression ratio. This resolves the contradiction by making precision adaptive rather than uniform, improving compression efficiency while maintaining necessary decoding quality.

The patent implements local quality by applying different quantization precision levels to different layers and components of the neural network based on their individual importance and characteristics. Critical layers receive higher precision while less critical layers use lower precision, optimizing the overall system performance. This localized approach to quality allocation directly addresses the contradiction by ensuring high decoding quality where needed while reducing bit allocation where possible.

The patent changes the quantization precision parameter dynamically for each layer based on computed statistics (mean, standard deviation) and importance metrics. Rather than using a fixed uniform precision, the system adapts precision levels to match the actual data distribution and significance of each layer, improving compression efficiency without significantly increasing device complexity through automated statistical analysis.

The patent performs preliminary analysis of neural network parameters and activations during the encoding phase, computing statistical properties and determining optimal quantization parameters before actual quantization occurs. This preliminary action allows the system to optimize bit allocation across layers in advance, achieving better compression efficiency while keeping the actual quantization process relatively simple.

The patent applies parameter changes by computing optimal bit allocation for each layer based on the variance and importance of parameters and activations. Layers with high variance or importance receive more bits for accurate quantization, while layers with low variance receive fewer bits. This adaptive parameter adjustment maintains quantization accuracy where needed while improving overall compression ratio through efficient bit distribution.

The patent applies partial action by allocating quantization bits selectively rather than uniformly to all layers. Critical layers that most impact reconstruction quality receive sufficient bit allocation for high accuracy, while less critical layers receive minimal allocation. This partial focus on essential components achieves acceptable quantization accuracy overall while maximizing compression ratio.