VLSI Synthesis Parameter Tuning via Iterative Cost Optimization
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Solution Overview
Problem
The process of tuning synthesis parameters for modern digital VLSI circuits is tedious and non-intuitive, especially for novice designers, as it involves numerous knobs and settings that require fine-tuning to achieve optimal results, which existing methods fail to automate effectively.
Innovation Solution
An automated system, SynTunSys, that selects a subset of parameter settings based on a tuning optimization cost function, runs synthesis jobs in parallel, analyzes results, creates combinations of settings, and iteratively refines them until convergence criteria are met, leveraging historical data and expert analysis to optimize design objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual parameter tuning is performed to achieve optimal synthesis results, then timing and power performance is improved, but design productivity deteriorates due to the tedious and time-consuming nature of the process
Solution Approach 1:
The system performs self-service by automatically tuning synthesis parameters through iterative evaluation and optimization without requiring manual designer intervention. The automated system evaluates multiple parameter settings, analyzes results, and refines settings autonomously to achieve optimal timing and power performance while eliminating the tedious manual tuning process
Solution Approach 2:
The system implements feedback mechanisms by continuously analyzing synthesis results and using this information to guide subsequent parameter adjustments. The iterative process evaluates timing and power metrics from each synthesis run and uses this feedback to refine parameter settings in the next iteration, progressively improving synthesis result quality
2Manufacturing precision
If comprehensive parameter tuning is performed to optimize synthesis results, then timing and power performance is improved, but the complexity of the design process increases due to the large number of parameters to tune
Solution Approach 1:
The system segments the complex parameter tuning process into manageable iterations, where each iteration focuses on evaluating and optimizing specific parameter settings. By dividing the comprehensive parameter space into discrete evaluation rounds and using parallel synthesis jobs, the system handles the complexity of hundreds or thousands of parameters systematically rather than overwhelming the design process
3Productivity
If automated parameter tuning systems are implemented to improve productivity, then design efficiency is improved, but the quality of results may deteriorate due to the difficulty of automating non-intuitive tuning decisions
Solution Approach 1:
The automated system performs self-service by autonomously evaluating synthesis results and making intelligent parameter adjustments based on observed performance metrics. The system learns from each iteration's outcomes and automatically refines parameter settings to improve timing and power performance, maintaining result quality while eliminating manual intervention
Solution Approach 2:
The system implements robust feedback loops that continuously monitor synthesis results for timing and power metrics. This feedback drives the iterative optimization process, allowing the automated system to make informed parameter adjustments that preserve or improve result quality while enhancing design efficiency
Data Source
AI summary
In one aspect, a method for tuning input parameters to a synthesis program is provided which includes the steps of: (a) selecting a subset of parameter settings for the synthesis program based on a tuning optimization cost function; (b) individually running synthesis jobs in parallel for each of the parameter settings in the subset; (c) analyzing results from a current iteration and prior iterations, if any, using the cost function; (d) using the results from the current iteration and the prior iterations, if any, to create combinations of the parameter settings; (e) running synthesis jobs in parallel for the combinations of the parameter settings in a next iteration; and (f) repeating the steps (c)-(e) for one or more additional iterations or until an exit criteria has been met.