Application Prototyping System Optimizing Pipeline Throughput
Find Innovative SolutionsGenerate Solutions
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
1Productivity
If manual user-involved approach is used for multiprocessor system architecture design, then user control and flexibility are maintained, but system performance and throughput are suboptimal due to unaccounted IO transfers and preprocessing times
Solution Approach 1:
The system performs self-service by automatically generating presentation models and executing them through inference engines to determine optimal multiprocessor architectures. The framework autonomously accounts for IO transfers, preprocessing, and postprocessing times without requiring manual user intervention, thereby achieving optimal throughput while reducing design complexity burden on users.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and storing presentation models that capture various architectural configurations and their performance characteristics. These pre-computed models are then queried and executed to determine the optimal architecture, avoiding the need for manual trial-and-error design iterations and enabling faster system deployment.
2Reliability
If comprehensive resource constraints and application stacks are considered in system design, then end-to-end performance is optimized, but computation and model creation time increase
Solution Approach 1:
The system performs preliminary action by pre-computing presentation models that encapsulate various architectural configurations and their performance characteristics under different resource constraints. These pre-generated models are stored and can be quickly queried during deployment, avoiding the need to re-analyze all constraints from scratch and significantly reducing model creation time while maintaining comprehensive performance analysis.
Solution Approach 2:
The system applies dynamics by creating modifiable presentation models with adjustable parameters that can be dynamically configured based on specific resource constraints. The inference engine can execute these models with different parameter settings to explore various architectural options, enabling flexible optimization without requiring complete re-analysis when constraints change.
3Productivity
If multiple presentation models are created and executed through inference engines, then optimal processing pipelines are identified, but computational overhead and system complexity increase
Solution Approach 1:
The system applies segmentation by dividing the complex design space into multiple discrete presentation models, each representing a specific architectural configuration. The inference engine executes these segmented models independently to evaluate their performance characteristics, allowing systematic comparison and selection of optimal configurations without overwhelming computational complexity.
Solution Approach 2:
The system utilizes parameter changes by creating presentation models with modifiable parameters that represent different architectural decisions (e.g., number of processors, memory allocation, IO configurations). The inference engine varies these parameters systematically to explore the design space and identify optimal configurations, transforming a complex design problem into a parameter optimization problem that can be solved methodically.
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, multiple presentation models at least in part based on identified processing metrics are created. In one aspect, the multiple presentation models include multiple processing pipelines configurable for execution on multiple computing devices. An inference engine can be used to provide an execution model for the multiple processing pipelines based at least in part on the multiple presentation models, with the execution model having improved processing metrics as compared to at least one of the multiple presentation models.