Design Space Modeling for Architecture Generation
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Solution Overview
Problem
Existing design modeling techniques face inefficiencies in generating architecture designs for complex systems due to exponential growth in design space combinations, leading to high processing time and resource requirements, and often result in low-quality or unuseable solutions.
Innovation Solution
A multi-stage process involving the transformation of a design space problem into a mathematically constrained model, followed by ensemble generation using a Boolean Satisfiability Problem (SAT) solver, significantly reducing processing time by limiting combinations and enabling parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational design synthesis is used to generate all feasible design combinations, then complete design coverage is achieved, but processing time and resource requirements become prohibitive due to exponential growth in design space combinations
Solution Approach 1:
The patent divides the design space into multiple segments or partitions, each representing a subset of the total design combinations. Instead of evaluating all combinations simultaneously, the system processes segments sequentially or in parallel, dramatically reducing the computational burden at any given time while maintaining comprehensive design coverage across all segments.
Solution Approach 2:
The system performs preliminary filtering and pruning of the design space before full evaluation. By identifying and eliminating infeasible combinations early in the process based on constraint satisfaction, the system reduces the number of combinations that require detailed analysis, thereby reducing processing time while preserving all viable design options.
2Productivity
If grammar based methods are used to generate designs quickly, then processing speed is improved, but solution quality deteriorates as most generated architectures are not useable
Solution Approach 1:
The system incorporates feedback mechanisms that evaluate generated designs against domain-specific constraints and criteria. When a generated architecture fails to meet requirements, the feedback loop guides subsequent generation attempts to avoid similar failures, progressively improving solution quality while maintaining efficient generation speeds through learned patterns from previous evaluations.
Solution Approach 2:
The design generation system dynamically adjusts its parameters and strategies based on the quality of generated solutions. When solution quality is low, the system modifies generation parameters, applies additional constraints, or switches between different generation methods to improve the proportion of usable architectures while adapting to maintain efficient processing throughput.
3Measurement precision
If declarative approaches are used to explicitly specify design constraints, then solution accuracy is improved, but processing time increases exponentially for complex problems
Solution Approach 1:
The patent introduces an intermediary layer between the declarative constraint specifications and the design generation process. This intermediary translates high-level constraints into optimized intermediate representations that can be processed more efficiently, maintaining the accuracy and explicitness of constraint satisfaction while reducing the exponential time complexity through smarter intermediate processing steps.
Data Source
AI summary
A system and method for using modeling language for efficient generation of design architectures in a design system are provided. A design space problem is received by the design system. The design space problem is transformed into a design space model. One or more ensembles are generated from the design space model. One or more design architectures are generated from the ensembles.