Automated HDL Code Generation for Hardware Model Optimization
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Solution Overview
Problem
Current systems lack an efficient method for automatically generating optimized hardware description language (HDL) code for executable models, particularly in modeling environments, which often require manual intervention and are not fully automated in satisfying constraints related to timing, area, and power consumption.
Innovation Solution
A system and method that utilize a hardware implementation training tool and code generator to automatically synthesize target hardware by analyzing models, applying optimization techniques, and generating HDL code that is bit-true and cycle-accurate, using a macro library to map performance characteristics and constraints, and iteratively optimizing the model until constraints are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used for hardware optimization, then flexibility and control are improved, but productivity and automation level deteriorate
Solution Approach 1:
The system enables self-service through automated optimization where the hardware optimization system automatically generates HDL code, applies optimizations, and satisfies constraints without requiring manual intervention from designers, thereby achieving high productivity while maintaining operational flexibility through automated decision-making
Solution Approach 2:
The system implements feedback mechanisms where performance characteristics are automatically measured and fed back into the optimization process, allowing the system to iteratively improve hardware implementations by comparing actual performance against constraints and adjusting optimizations accordingly
2Productivity
If automated code generation is implemented, then productivity is improved, but manufacturing precision and constraint satisfaction deteriorate
Solution Approach 1:
The system applies parameter changes by automatically adjusting multiple hardware parameters including timing constraints, area optimizations, and power consumption parameters through automated optimization algorithms that modify HDL code generation parameters to satisfy all constraints while maintaining high productivity
Solution Approach 2:
The system performs preliminary actions by pre-calculating and pre-optimizing hardware implementations before final code generation, where performance characteristics are measured in advance and optimization strategies are prepared beforehand to ensure constraint satisfaction is achieved automatically
3Manufacturing precision
If comprehensive optimization is applied, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex optimization process into distinct modules including performance characteristic measurement, optimization strategy generation, HDL code generation, and constraint verification, where each segment handles specific aspects of optimization independently to maintain high precision while managing overall system complexity through modular architecture
Data Source
AI summary
Systems and methods automatically generate optimized hardware description language code for a model created in a modeling environment. A training tool selects and provides scripts to a hardware synthesis tool chain that direct the tool chain to synthesize hardware components for core components of the modeling environment. A report generated by the tool chain is evaluated to extract performance data for the core components, and the performance data is stored in a library. An optimization tool estimates the performance of the model using the performance data in the library. Based on the performance estimate and an analysis of the model, the optimization tool selects an optimization technique which it applies to the model generating a revised. Estimating performance, and selecting and applying optimizations may be repeated until a performance constraint is satisfied or a termination criterion is met.