Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-Core Systems;

This technology involves task scheduling to optimally utilize the processing elements at runtime using imitation learning. Background: Harvesting the full potential of many-core systems-on-chips is difficult. The goals are to minimize execution t…

This technology involves task scheduling to optimize the processing elements at runtime using imitation learning.

Background:
Harvesting the full potential of many-core systems-on-chips is difficult. The goals are to minimize execution time, power dissipation, and energy consumption. This technology intends to solve this problem by assigning tasks to specific processing elements using imitation learning instead of reinforcement learning.

Applications:

  • Optimized processing elements
  • System-on-Chips

Advantages:

  • Minimized execution time
  • Less power dissipation
  • Lower energy consumption
  • The optimized potential of system-on-chips

Website

https://arizona.technologypublisher.com/tech?title=Runtime_Task_Scheduling_using_Imitation_Learning_for_Heterogeneous_Many-Core_Systems%3b

Contact Information

TTO Home Page: https://arizona.technologypublisher.com

Name: Brett Mortenson

Title: Licensing Manager, College of Engineering

Email: BrettM@tla.arizona.edu