Iterative Architecture Design Using Self-Optimizing Analytical Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing system architecture design methods struggle to efficiently optimize key hardware features for workloads, often relying on laborious processes and resource-intensive simulations, which can lead to biased models and delayed identification of optimal design points.
Innovation Solution
The implementation of iterative guided architecture design based on self-optimizing analytical models, which involves determining a figure of merit for a reference architecture, performing roofline analysis, estimating performance on a target architecture, and validating the model across multiple architectures to identify optimal design points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If resource-intensive simulations are used to optimize hardware features, then manufacturing precision of architecture design is improved, but productivity deteriorates due to laborious processes and time consumption
Solution Approach 1:
The patent creates analytical models that are simplified copies of the complex hardware system, allowing performance prediction without running full resource-intensive simulations. These models capture essential system behavior while enabling rapid evaluation of design alternatives.
Solution Approach 2:
The patent performs preliminary analytical modeling and validation before final architecture optimization. By establishing validated analytical models early in the design process, the system prepares prediction tools that can quickly evaluate design options without requiring subsequent full simulations.
2Measurement precision
If traditional design methods are used, then device complexity is managed, but measurement precision of performance metrics deteriorates due to biased models and delayed identification of optimal design points
Solution Approach 1:
The patent implements iterative validation where analytical model predictions are compared against actual simulation or measurement data. Discrepancies feed back into model refinement, progressively improving prediction accuracy while maintaining model tractability through targeted adjustments.
Solution Approach 2:
The analytical models are designed to be adaptive and iterative rather than static. The validation process allows models to evolve and improve their accuracy over time as more data becomes available, enabling dynamic refinement of prediction precision without requiring complete model reconstruction.
Data Source
AI summary
Provided are systems, methods, and apparatuses for iterative guided architecture design based on self-optimizing analytical models. In one or more examples, the systems, devices, and methods include determining a figure of merit (FOM) of a reference architecture based on executing a workload on the reference architecture and measuring a hardware event associated with executing the workload. In some examples, the systems, devices, and methods include determining an analytical model based on the FOM and based on performing roofline analysis on the reference architecture. In some examples, the systems, devices, and methods include estimating performance of the workload on the target architecture based on the analytical model identifying an optimal design based on validating the analytical model on a plurality of architectures executing the workload.