Machine Learning Enhanced Compiler for FPGA Prototyping
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Solution Overview
Problem
Compilation of complex circuit designs for FPGA-based emulation and prototyping systems is time-intensive and resource-consuming, often leading to long processing times and potential failures due to netlist congestion and inadequate routability considerations.
Innovation Solution
A machine-learning enhanced compiler applies multiple machine learning models at various phases of the compilation process to predict and adjust placement and routing strategies, reducing compilation time and resource consumption by selecting the most suitable model based on distance calculations and cost functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional compilation methods are used for complex circuit designs, then the compilation process is thorough and complete, but the compilation time becomes excessively long and resource consumption increases
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict compilation time and routability metrics before the actual compilation process. This allows the system to identify potential issues and adjust placement and routing strategies in advance, avoiding lengthy compilation cycles and reducing overall compilation time while maintaining thoroughness.
Solution Approach 2:
The patent implements feedback mechanisms where machine learning models continuously predict compilation metrics during the compilation process. These predictions provide feedback that allows dynamic adjustment of compilation parameters and strategies, enabling the system to maintain high reliability while reducing unnecessary processing time for designs that are likely to succeed.
2Reliability
If detailed placement and routing strategies are applied to prevent netlist congestion, then the routability improves, but the compilation time increases due to additional processing
Solution Approach 1:
The patent uses machine learning models to predict routability metrics and identify potential netlist congestion issues before detailed placement and routing is performed. This preliminary analysis allows the system to focus detailed strategies only on critical areas, improving routability while minimizing the time spent on comprehensive detailed processing.
Solution Approach 2:
The patent applies local quality by using machine learning predictions to identify specific regions or components that require detailed placement and routing attention. Instead of applying uniform detailed strategies across the entire design, the system concentrates resources on areas with predicted routing issues, improving overall routability efficiency.
3Productivity
If multiple machine learning models are applied at various compilation phases, then the compilation time and resource usage are reduced, but the system complexity increases
Solution Approach 1:
The patent segments the compilation process into distinct phases, applying specific machine learning models at each phase (e.g., early placement, detailed placement, routing). This segmentation allows the system to use lightweight prediction models for early phases and more specialized models only when needed, improving overall efficiency while managing system complexity through modular organization.
Solution Approach 2:
The patent employs universal machine learning model architectures that can serve multiple functions across different compilation phases. By designing models that can adapt to different stages of compilation, the system reduces the need for entirely separate complex systems for each phase, thereby improving productivity while controlling overall system complexity.
Data Source
AI summary
A method includes storing a base model generated using base data and receiving training data generated by compiling circuit designs. The method also includes generating, using the training data, a tuned model and generating, using the training data and the base data, a hybrid model. The method further includes receiving a selected cost function and biasing the base model, the tuned model, and the hybrid model using the selected cost function.