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

Case ID:

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

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 through assigning tasks to specific processing elements using imitation learning instead of the commonly used reinforcement learning.


  • Optimized processing elements
  • System-on-Chips


  • Minimized execution time
  • Less power dissipation
  • Lower energy consumption
  • Optimized potential of system-on-chips
Patent Information:
Contact For More Information:
Tariq Ahmed
Sr Licensing Manager, College of Engineering
The University of Arizona
Lead Inventor(s):
Umit Ogras
Radu Marculescu
Ali Akoglu
Chaitali Chakrabarti
Daniel Bliss
Samet Egemen Arda
Anderson Luiz Sartor
Nirmal Kumbhare
Anish Krishnakumar
Joshua Mack
Ahmet Alper Goksoy
Sumit Kumar Mandal