Neural Network Weight Quantization via Adaptive Block Partitioning

The patent applies parameter changes by dynamically adjusting the bit depth of weight coefficients based on their statistical distribution. Instead of using a fixed high bit depth for all coefficients, the system analyzes the actual range and distribution of weights in each layer, then selects an optimal bit depth that maintains sufficient precision while minimizing memory consumption. This resolves the contradiction by adapting the precision parameter to the actual needs of each neural network layer.

The patent implements local quality by applying different bit depths to different layers or blocks of the neural network based on their specific characteristics. Important layers that require high precision maintain higher bit depths, while less critical layers use lower bit depths. This selective approach ensures that memory is allocated efficiently according to the actual precision requirements of each part of the network, rather than uniformly across all layers.

The patent changes the bit depth parameter dynamically based on the computational requirements and performance constraints. By reducing bit depth where high precision is not critical, the system decreases the computational complexity of matrix operations, thereby improving inference speed without significantly compromising overall model accuracy. This resolves the productivity-precision contradiction by adapting precision to functional requirements.

The patent applies partial action by using high precision only where necessary and lower precision elsewhere. Instead of maintaining high bit depth throughout the entire network, the system identifies critical paths and layers that benefit most from high precision and applies reduced precision in other areas. This selective precision strategy maintains adequate performance while improving overall inference speed.

The patent applies local quality by using different quantization strategies for different layers or blocks of the neural network. Instead of a single uniform quantization approach, the system analyzes the distribution characteristics of weights in each layer and applies tailored quantization methods. This allows each layer to maintain optimal precision while keeping the overall system manageable in complexity.

The patent changes quantization parameters such as step size and number of levels based on the statistical properties of each layer's weight coefficients. By adapting these parameters locally rather than using fixed uniform quantization, the system reduces information loss in critical layers while maintaining reasonable complexity. This resolves the contradiction by making quantization adaptive rather than rigid.

The patent applies segmentation by dividing the neural network into multiple blocks or layers and applying different quantization and compression strategies to each segment. This allows the system to optimize each block independently based on its characteristics, improving overall compression efficiency. The segmentation approach manages complexity by breaking down the large-scale optimization problem into smaller, more manageable sub-problems.

The patent implements local quality by applying different partitioning granularities and optimization techniques to different blocks of the network. Critical blocks receive more detailed partitioning and optimization, while less important blocks use coarser partitioning. This selective approach improves compression efficiency where needed while limiting the increase in overall system complexity.