Qualcomm incorporated (20240119301). NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING simplified abstract
Contents
- 1 NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING - 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
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING
Organization Name
Inventor(s)
Wonseok Jeon of San Diego CA (US)
Mukul Gagrani of Milpitas CA (US)
Weiliang Zeng of San Diego CA (US)
Edward Teague of San Diego CA (US)
Burak Bartan of San Jose CA (US)
Piero Zappi of La Jolla CA (US)
Christopher Lott of San Diego CA (US)
NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119301 titled 'NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING
Simplified Explanation
The abstract describes a processor-implemented method for scheduling tasks in an artificial neural network based on node priorities sampled from a computation graph.
- Sampling node priorities from a computation graph according to a priority sampling policy.
- Converting node priorities to a schedule using a list scheduling function.
- Associating each node with a processor from a group of processors to optimize makespan.
- Performing tasks in the artificial neural network based on the generated schedule.
Potential Applications
This technology can be applied in various fields such as:
- Artificial intelligence
- Machine learning
- Task scheduling optimization
Problems Solved
- Efficient task scheduling in artificial neural networks
- Optimizing processor utilization in computational tasks
Benefits
- Improved performance of artificial neural networks
- Reduced processing time for tasks
- Enhanced resource utilization in devices
Potential Commercial Applications
Optimized Task Scheduling in Artificial Neural Networks: Improving Efficiency and Performance
Possible Prior Art
Prior research may exist in the field of task scheduling algorithms for computational graphs and artificial neural networks.
Unanswered Questions
How does this method compare to existing task scheduling algorithms in terms of efficiency and performance?
The article does not provide a comparison with existing algorithms to evaluate the effectiveness of the proposed method.
What are the specific criteria used in the priority sampling policy to sample node priorities from the computation graph?
The abstract does not detail the specific criteria or parameters used in the priority sampling policy for node priorities.
Original Abstract Submitted
a processor-implemented method includes sampling, according to a priority sampling policy, a set of node priorities from a computation graph. each node priority of the set of node priorities may be associated with a respective node on the computation graph. additionally, each node may represent an operation of a task performed by an artificial neural network. the method also includes converting, via a list scheduling function, the node priorities to a schedule that associates each node of the computation graph with a processor of a group of processors of a device associated with the artificial neural network, the schedule associated with a makespan. the method further includes performing the task in accordance with the schedule.
