Configurable GPU Pipeline for Systolic and Normal Calculation

The patent implements a unified processing architecture where the same computational units handle both traditional graphics processing tasks and machine learning computations. The processing clusters can dynamically switch between rendering operations and data processing operations, allowing a single GPU to serve multiple purposes without requiring separate specialized hardware for each function type.

The processor employs dynamic configuration of processing pipelines, where the same hardware resources can be reconfigured on-the-fly to handle different types of computations. The architecture allows runtime adjustment of processing modes, enabling seamless transition between graphics rendering and machine learning workloads based on the current task requirements.

The processor is divided into multiple independent processing clusters, each capable of handling specific computational tasks. Within each cluster, functional units are segmented to handle different operation types (arithmetic, logic, data movement). This segmentation allows parallel execution of multiple operations simultaneously, improving overall processing efficiency while keeping each individual unit relatively simple.

The patent combines multiple specialized functions into integrated processing units. For example, arithmetic logic units are merged with data movement capabilities and control logic into unified processing elements. This merging reduces the number of separate components needed while maintaining high processing efficiency through coordinated operation of integrated functions.