FPGA Logic Synthesis Optimization via AI Design Explorer
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Solution Overview
Problem
Current FPGA design flows face challenges in optimizing logic synthesis due to the need for unique recipes for each design, leading to inefficiencies in LUT resource utilization and performance, particularly in Field-Programmable Gate Arrays (FPGAs), where existing tools struggle to find the optimal synthesis script for varying logic structures.
Innovation Solution
The introduction of a design explorer that uses artificial intelligence and parallel exploration techniques to dynamically build synthesis recipes, integrating with logic synthesizers like ABC or LSOracle, enabling breadth-first exploration and adaptive transformation sequences to improve LUT mapping and logic level reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard recipes are used for logic synthesis, then ease of operation is improved, but manufacturing precision deteriorates
Solution Approach 1:
The patent implements dynamic recipe generation that adapts to each specific logic design rather than using static standard recipes. The system automatically adjusts transformation sequences based on the characteristics of the input logic network, enabling both ease of operation and high manufacturing precision simultaneously.
Solution Approach 2:
The system changes parameters of the synthesis process by dynamically selecting and sequencing transformations based on design-specific parameters. This allows the recipe to be optimized for each particular logic design while maintaining ease of use through automation.
2Manufacturing precision
If unique recipes are created for each design, then manufacturing precision is improved, but device complexity worsens
Solution Approach 1:
The system provides self-service by automatically generating unique optimization recipes for each design without requiring external expert intervention. The automated recipe generation engine analyzes the logic network and creates optimal transformation sequences independently, achieving high manufacturing precision while avoiding the complexity of manual recipe creation.
Solution Approach 2:
The patent replaces the mechanical process of manual recipe creation by experts with an automated computational system. This substitution eliminates the complexity associated with human expert involvement while maintaining or improving manufacturing precision through algorithmic optimization.
3Manufacturing precision
If extensive logic optimization is performed, then manufacturing precision is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary actions by pre-evaluating transformation options and sequencing them optimally before full optimization execution. This preliminary analysis enables the system to achieve high manufacturing precision more efficiently by avoiding unnecessary transformations and focusing computational effort on the most promising optimization paths.
Solution Approach 2:
The patent maintains continuity of useful action through iterative optimization that continuously refines the logic network. The system performs transformations in continuous passes, building upon previous results to achieve progressive improvement without redundant operations, thereby reducing total optimization time while maintaining high precision.
4Ease of manufacture
If standard recipes are used across many designs, then ease of manufacture is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual recipe creation and selection processes with automated systems that generate optimization recipes programmatically. This substitution maintains ease of manufacture through automation while significantly improving productivity by eliminating the time-consuming process of creating unique recipes for each design.
Solution Approach 2:
The system dynamically changes parameters of the synthesis process based on each design's characteristics, enabling automated adaptation that improves productivity. The parameter-driven approach allows the system to maintain ease of manufacture through automation while achieving design-specific optimization that standard recipes cannot provide.
Data Source
AI summary
Technology is described for improvement of a Boolean Network. The method can include applying a plurality of transformation scripts to a Boolean Network to form a plurality of levels of a transformation tree with nodes representing transformation metrics for the transformation scripts applied to the Boolean Network. The nodes in individual levels of the transformation tree can be prioritized based in part on a cost function that uses the transformation metrics to identify an improved node as compared to less improved nodes in each of the plurality of levels of the transformation tree. Another operation may be identifying a transformation script using improved nodes of the transformation tree.