Performance Prediction Report for Block Diagram Partitioning
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Solution Overview
Problem
Partitioning block diagram models across multiple computational hardware components is challenging due to varying processor capabilities, memory access rates, and interface types, leading to inefficient manual trial-and-error processes without assurance of optimal solutions, and there is a need for a quantitative assessment of partitioning schemes before code generation and execution.
Innovation Solution
A method and system generate a performance prediction report that analyzes target hardware platform attributes and computational requirements to estimate memory utilization, interface loading, and throughput of a user-defined partitioning scheme, allowing users to modify the scheme to achieve specific goals without explicit implementation trials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual trial-and-error partitioning is used, then flexibility in exploring different partitioning schemes is achieved, but time consumption and cost increase significantly
Solution Approach 1:
The patent performs preliminary analysis of computational requirements, memory access patterns, and interface characteristics before actual code generation. By predicting performance metrics in advance and identifying optimal partitioning schemes through simulation, the system eliminates the need for time-consuming manual trial-and-error implementation on actual hardware, thus resolving the contradiction between exploration flexibility and time consumption.
2Ease of manufacture
If arbitrary partitioning of block diagram model is used, then implementation simplicity is achieved, but design requirements may not be met
Solution Approach 1:
The patent systematically varies partitioning parameters such as computational load distribution, memory allocation, and interface configuration to find optimal configurations. By analyzing how different parameter combinations affect performance metrics like throughput, latency, and resource utilization, the system ensures design requirements are met while maintaining implementation simplicity through automated code generation from the optimized partitioning scheme.
3Power
If multiple computational hardware components are used, then processing capacity and performance are improved, but partitioning complexity increases
Solution Approach 1:
The patent divides the block diagram model into distinct partitions that can be independently assigned to different computational hardware components. By segmenting the model based on computational requirements, data flow patterns, and resource utilization, the system manages partitioning complexity while effectively utilizing multiple hardware components to achieve improved processing capacity and performance.
Data Source
AI summary
A method and system are described for generating a performance prediction report to assist finalizing a partitioning scheme of a block diagram model. Providing a user-defined partitioning scheme and information describing a target hardware platform used in that partitioning scheme, the present invention can generate a performance prediction report by analyzing the computational characteristics of the block diagram model.