View source for 18464996. NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING simplified abstract (QUALCOMM Incorporated)

=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 18464996 titled 'NEURAL DIRECTED ACYCLIC GRAPH (DAG) SCHEDULING VIA ONE-SHOT PRIORITY SAMPLING

==Simplified Explanation==

The abstract describes a method for scheduling tasks in an artificial neural network based on node priorities sampled from a computation graph.

* Sampling of 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:
* Optimization of task scheduling in artificial neural networks.
* Improving the efficiency and performance of neural network operations.

== Problems Solved ==
* Efficient allocation of tasks to processors in artificial neural networks.
* Optimizing the overall performance of the neural network by reducing makespan.

== Benefits ==
* Increased efficiency in task scheduling.
* Enhanced performance of artificial neural networks.
* Reduction in processing time and resource utilization.

== Potential Commercial Applications ==
Optimized task scheduling in artificial neural networks can be beneficial for:
* Cloud computing services.
* Data centers.
* Machine learning applications.

== Possible Prior Art ==
Prior art in task scheduling algorithms for parallel processing systems may exist, but specific examples are not provided in the abstract.

=== Unanswered Questions ===

=== How does this method compare to existing task scheduling algorithms in terms of efficiency and performance? ===
The abstract does not provide a direct comparison with existing algorithms, leaving the question of its superiority unanswered.

=== What impact does the scheduling policy have on the overall performance of the artificial neural network? ===
The abstract does not delve into the specific effects of the scheduling policy on the network's performance, leaving this aspect open for further exploration.


==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]]