Stimulus Space Clustering for Reduced IC Simulation Inputs
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Solution Overview
Problem
Current parallel graphics data processing systems face challenges in efficiently handling diverse operations on graphics data, such as linear interpolation, tessellation, and depth testing, particularly in systems with programmable and fixed function computational units.
Innovation Solution
The use of a general-purpose graphics processing unit (GPU) to perform data processing via clustering and stochastic processes, enabling efficient summarization of stimulus space by leveraging SIMT architectures and dedicated circuitry for processing commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed function computational units are used for graphics processing, then processing efficiency for specific operations is improved, but adaptability to diverse operations deteriorates
Solution Approach 1:
The patent implements a general-purpose graphics processing unit with programmable computational units that can execute diverse graphics operations (linear interpolation, tessellation, depth testing, etc.) through a unified architecture. This allows a single hardware design to handle multiple operation types, eliminating the need for separate fixed-function units for each operation while maintaining high processing efficiency through optimized execution engines.
2Adaptability or versatility
If programmable computational units are implemented, then adaptability to diverse operations is improved, but processing efficiency for specific operations deteriorates
Solution Approach 1:
The patent segments the computational units into specialized execution engines (such as vertex execution units, fragment execution units, and geometry execution units) that can be independently configured. Each execution engine is optimized for specific operation types while remaining programmable, allowing the system to achieve high efficiency for particular operations by activating only the necessary specialized units rather than requiring all units to be fully programmable.
3Productivity
If pipelining is implemented to process graphics data in parallel, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements nested pipelines where multiple execution pipelines are organized in a hierarchical structure. Execution units are nested within larger processing blocks, which are in turn nested within the overall graphics pipeline. This nested organization allows parallel processing to be achieved through systematic repetition of modular units, reducing overall complexity by breaking down the pipeline into manageable, self-similar components that can be independently configured and optimized.
Data Source
AI summary
An apparatus to facilitate summarizing stimulus space via clustering and stochastic processes is disclosed. The apparatus includes processing circuitry to: receive an original set of stimuli input data, the stimuli input data comprises variables for input to an electronic circuit simulator to check an integrity of integrated circuit designs and to predict integrated circuit behavior; apply a clustering algorithm to the original set to determine k subsets of the original set; define, for each subset of the k subsets, a random variable per subset in accordance with a distribution corresponding to the subset; compute inter-cluster transition probabilities between the k subsets; sample, in accordance with the inter-cluster transition probabilities, a reduced set of the stimuli input data from the k subsets of the original set; and utilize the reduced set as a smaller representative set of the stimuli input data for input to the electronic circuit simulator.