Autonomous Multiprocessor Architecture Generator
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Solution Overview
Problem
Contemporary multiprocessor system design methodologies rely on manual, user-involved approaches that do not consider associated application stacks or host functions, leading to suboptimal performance in end-to-end system deployments due to unaccounted IO transfers, preprocessing, and post-processing times.
Innovation Solution
The system identifies resource constraints for multiple computing devices to create presentation models with modifiable parameters, using inference engines to execute neural network models and provide execution models for processing pipelines, thereby improving processing metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user-involved approach is used for multiprocessor system design, then ease of operation is improved, but productivity deteriorates due to suboptimal performance in end-to-end system deployments
Solution Approach 1:
The system performs self-service by automatically generating presentation models and execution models without requiring manual user intervention. The pipeline processing architecture generator autonomously identifies resource constraints, creates optimized processing pipelines, and generates execution models that account for all system components including IO transfers, preprocessing, and postprocessing operations.
Solution Approach 2:
The patent replaces the manual mechanical design process with an automated computational system. Instead of users manually designing multiprocessor architectures, the system uses an inference engine and pipeline processing architecture generator to automatically create optimized architectures, substituting human manual work with automated software-based design generation.
2Manufacturing precision
If comprehensive resource constraints are considered in presentation model creation, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex design process into distinct components: resource constraint identification, presentation model generation, execution model creation, and pipeline optimization. By dividing the comprehensive design task into manageable segments handled by different modules (pipeline processing architecture generator, inference engine), the system achieves high precision while managing complexity through modular architecture.
Solution Approach 2:
The pipeline processing architecture generator acts as an intermediary between resource constraints and execution models. It translates high-level resource constraints into detailed presentation models and execution models, serving as a mediator that handles the complexity of comprehensive resource consideration while providing precise design outcomes.
3Productivity
If automated pipeline processing architecture generator is used, then productivity is improved, but ease of operation deteriorates due to increased system complexity
Solution Approach 1:
The system creates simplified copies or representations of complex system behaviors through presentation models and execution models. These models are simplified abstractions that capture essential resource constraints and performance characteristics without requiring users to understand the full complexity of the underlying multiprocessor architecture, making the system easier to operate while maintaining high productivity.
Data Source
AI summary
Application prototyping systems and methods are disclosed. One aspect is a processing method for multiple computing devices that includes identifying resource constraints for the multiple computing devices. Using identified resource constraints, a presentation model having a plurality of modifiable parameters based at least in part based on the resource constraints is created. At least one inference engine supporting neural network processing is used to execute a particular neural network model based at least in part on the presentation model.