Pipelined Binary Search Quantization for Low-Memory Neural Inference

The patent segments the threshold values into multiple levels organized as a binary search tree structure. Instead of storing all threshold values in a flat exponential array, the thresholds are divided into hierarchical levels where each level contains a subset of thresholds. This segmentation reduces the memory storage requirement from exponential to linear with respect to bit-width, while maintaining the ability to perform accurate multi-thresholding operations through the tree traversal mechanism.

The patent implements a dynamic pipelined binary search mechanism where multiple comparison operations are performed concurrently across different pipeline stages. Instead of waiting for sequential comparisons to complete, the system dynamically progresses through multiple comparison stages in parallel, with each stage processing a different portion of the search space. This dynamic approach maintains reduced memory storage while significantly decreasing computational time through concurrent operations.

The patent performs preliminary organization of threshold values into a binary search tree structure during system initialization or model loading. This preliminary action pre-computes and stores the hierarchical threshold arrangement, enabling subsequent inference operations to efficiently traverse the pre-organized structure. By performing this organizational work in advance, the system enables faster inference latency for wider bit-width quantizations without sacrificing accuracy, as the traversal of pre-organized thresholds is more efficient than on-the-fly computation.