Neural Boolean Expression Optimization for Integrated Circuits
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Solution Overview
Problem
Conventional logic optimization methods for integrated circuits are inadequate for large and complex designs, often failing to consider the vast space of possible logical functions and requiring heuristic approaches that may introduce errors, leading to suboptimal hardware implementations.
Innovation Solution
Employing a language model neural network trained on Boolean expression processing tasks to generate alternative Boolean expressions with fewer operators while maintaining logical equivalence, thereby optimizing integrated circuit designs through automated logic optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional heuristic-based logic optimization methods are used, then the design process is simpler to implement, but the optimization quality deteriorates for large and complex chip designs
Solution Approach 1:
The patent replaces conventional heuristic-based mechanical search methods with a neural network system that uses learned patterns and probabilistic reasoning to generate optimized logic expressions. The neural network substitutes the step-by-step heuristic search process with a direct mapping from input expressions to optimized outputs, achieving both high quality and scalability.
Solution Approach 2:
The patent transforms the optimization problem by changing the approach from deterministic heuristic rules to probabilistic neural network predictions. By training the network on diverse logic optimization examples, the system learns to predict optimized expressions that generalize to large complex designs, overcoming the limitations of fixed heuristic rules.
2Use of energy by moving object
If heuristic-based optimization methods are used, then computational resources are consumed less during optimization, but the coverage of logical function space deteriorates
Solution Approach 1:
The patent performs preliminary training of the neural network on a comprehensive dataset of logic optimization examples before deployment. This pre-computation phase allows the network to internalize diverse optimization patterns and strategies, enabling it to efficiently handle new designs without requiring extensive computational resources during the actual optimization process while maintaining broad coverage of logical function spaces.
3Loss of energy
If the number of logic gates is reduced, then power consumption decreases, but the design complexity increases
Solution Approach 1:
The patent employs an automated neural network system that independently performs logic optimization without requiring manual intervention or complex human expert involvement. The system self-manages the entire optimization process, from analyzing the input design to generating optimized logic expressions, thereby reducing power consumption while avoiding the increased design complexity that would result from manual optimization efforts.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural network-assisted circuit design for integrated circuits. One of the methods include obtaining a description of an integrated circuit (IC); selecting one or more Boolean expressions specified in the description; and processing the one or more selected Boolean expressions using a neural network to generate an output that comprises one or more alternative Boolean expressions.