View source for Qualcomm incorporated (20240119301). NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING simplified abstract

=NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING=


==Organization Name==

[[:Category:qualcomm incorporated|qualcomm incorporated]]


[[Category:qualcomm incorporated]]

==Inventor(s)==

[[:Category:Wonseok Jeon of San Diego CA (US)|Wonseok Jeon of San Diego CA (US)]][[Category:Wonseok Jeon of San Diego CA (US)]]

[[:Category:Mukul Gagrani of Milpitas CA (US)|Mukul Gagrani of Milpitas CA (US)]][[Category:Mukul Gagrani of Milpitas CA (US)]]

[[:Category:Weiliang Zeng of San Diego CA (US)|Weiliang Zeng of San Diego CA (US)]][[Category:Weiliang Zeng of San Diego CA (US)]]

[[:Category:Edward Teague of San Diego CA (US)|Edward Teague of San Diego CA (US)]][[Category:Edward Teague of San Diego CA (US)]]

[[:Category:Burak Bartan of San Jose CA (US)|Burak Bartan of San Jose CA (US)]][[Category:Burak Bartan of San Jose CA (US)]]

[[:Category:Piero Zappi of La Jolla CA (US)|Piero Zappi of La Jolla CA (US)]][[Category:Piero Zappi of La Jolla CA (US)]]

[[:Category:Christopher Lott of San Diego CA (US)|Christopher Lott of San Diego CA (US)]][[Category: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.



[[Category:G06N3/092]]