Binary Decision Diagram Construction via Interleaved Search
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Solution Overview
Problem
Binary decision diagrams in computer-aided design suffer from exponential space complexity and resource consumption due to the reliance on total order in Boolean variables and the large number of 'alive' diagrams needed for circuit graph representations, leading to inefficiencies in memory usage.
Innovation Solution
A dynamic, resource-constrained and interleaved depth-first-search and modified breadth-first-search schedule is employed to build decision diagrams, where size limits are set for m-ary decision representations, switching between depth-first and breadth-first techniques to optimize resource utilization and reduce the number of live nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary decision diagrams are built using traditional depth-first or breadth-first techniques, then the logic function can be represented, but memory consumption and runtime increase due to exponential space complexity
Solution Approach 1:
The patent applies dynamics by making the construction schedule adaptive rather than static. The system dynamically switches between depth-first and breadth-first search techniques based on resource availability and problem characteristics. The schedule is refined iteratively using feedback from construction attempts, allowing the system to adapt its strategy to minimize memory consumption while ensuring complete logic function representation.
Solution Approach 2:
The patent changes parameters by adjusting the construction schedule parameters (search technique, node selection order, resource allocation) based on feedback from previous construction attempts. The system modifies these parameters iteratively to optimize the balance between memory consumption and representation completeness, transforming the fixed traditional approaches into flexible adaptive processes.
2Reliability
If all binary decision diagrams are kept alive for circuit graph representations, then complete Boolean reasoning can be performed, but resource consumption becomes excessive
Solution Approach 1:
The patent extracts only the essential binary decision diagrams needed for Boolean reasoning at any given time, rather than keeping all diagrams alive simultaneously. The selective construction approach identifies and builds only those diagrams necessary for the current reasoning task, discarding or suspending others to reduce memory consumption while maintaining sufficient Boolean reasoning capability.
Solution Approach 2:
The patent implements discarding and recovering by temporarily suspending the construction or retention of binary decision diagrams that are not currently needed, then recovering them later when required for subsequent reasoning tasks. This iterative process allows the system to manage resource consumption by selectively maintaining only the necessary subset of diagrams at any time.
3Ease of manufacture
If a total order is imposed on Boolean variables for binary decision diagram construction, then the diagrams can be systematically built, but the number of live diagrams increases unnecessarily
Solution Approach 1:
The patent replaces the static total order approach with a dynamic scheduling mechanism that adapts the construction order based on resource constraints and problem characteristics. Instead of following a fixed variable ordering, the system dynamically determines which diagrams to build and in what order, reducing the number of live diagrams while maintaining systematic construction through iterative refinement.
4Quantity of substance
If resource constraints are imposed on binary decision diagram construction, then memory consumption is reduced, but the risk of incomplete construction increases
Solution Approach 1:
The patent implements feedback by using the results of construction attempts to inform and refine the construction schedule. The system monitors resource consumption and construction progress, then uses this feedback to adjust the schedule for subsequent attempts, ensuring that resource constraints are respected while progressively achieving complete construction through iterative improvement.
Solution Approach 2:
The patent applies preliminary action by performing schedule refinement and resource allocation decisions before the actual construction process. The system prepares an optimized construction schedule that anticipates resource requirements, allowing construction to proceed within constraints while maximizing the likelihood of completeness through advance planning and iterative refinement.
Data Source
AI summary
A method, system and computer program product for building decision diagrams efficiently in a structural network representation of a digital circuit using a dynamic resource constrained and interleaved depth-first-search and modified breadth-first-search schedule is disclosed. The method includes setting a first size limit for a first set of one or more m-ary decision representations describing a logic function and setting a second size limit for a second set of one or more m-ary decision representations describing a logic function. The first set of m-ary decision representations of the logic function is then built with one of the set of a depth-first technique or a breadth-first technique until the first size limit is reached, and a second set of m-ary decision representations of the logic function is built with the other technique until the second size limit is reached. In response to determining that a union of first set and the second set of m-ary decision representations do not describe the logic function, the first and second size limits are increased, and the steps of building the first and second set are repeated. In response to determining that the union of the first set of m-ary decision representations and the second set of m-ary decision representations describe the logic function, the union is reported.