Neural Topological Ordering via Attention Graph Networks
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Solution Overview
Problem
Conventional approaches to finding optimal topological orders for tasks with precedence constraints are computationally complex and require extensive domain-specific knowledge, making them time-consuming and inefficient, especially for large-scale problems.
Innovation Solution
An end-to-end machine learning-based approach using an attention-based graph neural network encoder-decoder framework that generates topological orders by assigning scheduling priorities based on graph topology, reducing memory and energy consumption and improving performance with lower run times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional heuristic strategies are used to find optimal topological orders, then domain-specific knowledge can be applied, but the process becomes time-consuming and computationally complex
Solution Approach 1:
The patent replaces conventional mechanical/heuristic search methods with a neural network-based probabilistic model. The neural network learns to predict optimal topological orders directly from graph representations, substituting iterative heuristic search with a single forward pass through a trained model, thereby reducing time consumption while maintaining optimality.
Solution Approach 2:
The patent transforms the problem from discrete combinatorial optimization to continuous probability estimation. Instead of searching through discrete permutations, the neural network operates with continuous parameters (probabilities) to model the topological order distribution, enabling faster computation through gradient-based optimization during training.
2Adaptability or versatility
If handcrafted heuristic strategies are designed for each instance, then problem-specific input distributions can be addressed, but the process requires extensive domain knowledge and is time consuming
Solution Approach 1:
The patent creates a universal neural network model that can handle various problem-specific input distributions through a single trained architecture. The model learns general patterns from diverse datasets and can be applied to different problems without requiring handcrafted strategies, achieving both adaptability and simplicity.
Solution Approach 2:
The patent uses neural networks to copy and generalize domain knowledge from training data rather than manually encoding it. The model learns representations of problem structures and solutions from examples, capturing domain-specific patterns automatically without requiring explicit programming of domain knowledge for each instance.
3Manufacturing precision
If auto-regressive methods are used to generate topological orders, then detailed sequential decisions can be made, but memory consumption and run time increase significantly
Solution Approach 1:
The patent performs preliminary computation during the training phase, where the neural network learns to predict entire topological orders in one step. This pre-learning eliminates the need for sequential auto-regressive generation during inference, reducing both memory consumption and run time while maintaining decision accuracy.
Solution Approach 2:
The patent skips the intermediate auto-regressive generation steps by using a neural network that directly outputs the complete topological order or its probability distribution. This bypasses the iterative decision-making process of auto-regressive methods, rushing through the computation in a single forward pass and significantly reducing memory and time requirements.
Data Source
AI summary
A processor-implemented method for generating a topological order using an artificial neural network (ANN) includes receiving a set of tasks to be performed. The tasks are represented in a graph including multiple nodes connected by edges. Each node corresponds to a task in the set of tasks. A scheduling priority is assigned to each node in the graph. A next node of potential next nodes is selected according to a probability of each of the potential next nodes based on the assigned scheduling priorities and a topology of the graph. A topological order of the tasks is generated by repeating the selection of the next node.