18394307. GRAPH NEURAL NETWORK MODEL FOR NEURAL NETWORK SCHEDULING DECISIONS simplified abstract (Intel Corporation)
Contents
- 1 GRAPH NEURAL NETWORK MODEL FOR NEURAL NETWORK SCHEDULING DECISIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GRAPH NEURAL NETWORK MODEL FOR NEURAL NETWORK SCHEDULING DECISIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does the GNN model compare to traditional heuristic-based approaches in terms of efficiency and accuracy in predicting optimal parameters for DNN compilation?
- 1.9.3 What are the potential limitations or challenges in implementing the GNN-based scheduling process for DNN compilation in real-world applications?
- 1.10 Original Abstract Submitted
GRAPH NEURAL NETWORK MODEL FOR NEURAL NETWORK SCHEDULING DECISIONS
Organization Name
Inventor(s)
GRAPH NEURAL NETWORK MODEL FOR NEURAL NETWORK SCHEDULING DECISIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18394307 titled 'GRAPH NEURAL NETWORK MODEL FOR NEURAL NETWORK SCHEDULING DECISIONS
Simplified Explanation
A graph neural network (GNN) model is used in a scheduling process for compiling a deep neural network (DNN). The DNN, and parameter options for scheduling the DNN, are represented as a graph, and the GNN predicts a set of parameters that is expected to have a low cost. Using the GNN-based model, a compiler can produce a schedule for compiling the DNN in a relatively short and predictable amount of time, even for DNNs with many layers and/or many parameter options. For example, the GNN-based model reduces the overhead of exploring every parameter combination and does not exclude combinations from consideration like prior heuristic-based approaches.
- The innovation involves using a graph neural network (GNN) model to optimize the scheduling process for compiling deep neural networks (DNNs).
- The GNN predicts a set of parameters that result in a low cost, enabling efficient compilation of DNNs with multiple layers and parameter options.
- This approach reduces exploration overhead and considers all parameter combinations, unlike previous heuristic-based methods.
Potential Applications
The technology can be applied in:
- Deep learning model compilation
- Optimization of scheduling processes in neural network development
Problems Solved
- Efficient compilation of deep neural networks
- Reduction of exploration overhead in parameter optimization
Benefits
- Faster and more predictable compilation of DNNs
- Improved optimization of scheduling processes
- Enhanced efficiency in neural network development
Potential Commercial Applications
Optimizing DNN compilation processes for:
- Tech companies
- Research institutions
Possible Prior Art
Prior art may include:
- Heuristic-based approaches for scheduling DNN compilation
- Traditional optimization methods for parameter selection in neural networks
Unanswered Questions
How does the GNN model compare to traditional heuristic-based approaches in terms of efficiency and accuracy in predicting optimal parameters for DNN compilation?
The article does not provide a direct comparison between the GNN model and traditional heuristic-based approaches in terms of efficiency and accuracy.
What are the potential limitations or challenges in implementing the GNN-based scheduling process for DNN compilation in real-world applications?
The article does not address potential limitations or challenges that may arise in implementing the GNN-based scheduling process for DNN compilation in real-world applications.
Original Abstract Submitted
A graph neural network (GNN) model is used in a scheduling process for compiling a deep neural network (DNN). The DNN, and parameter options for scheduling the DNN, are represented as a graph, and the GNN predicts a set of parameters that is expected to have a low cost. Using the GNN-based model, a compiler can produce a schedule for compiling the DNN in a relatively short and predictable amount of time, even for DNNs with many layers and/or many parameter options. For example, the GNN-based model reduces the overhead of exploring every parameter combination and does not exclude combinations from consideration like prior heuristic-based approaches.