Abstract: Methods and systems are described that improve simulations which use thermal models to test dynamic thermal mitigation of devices, such as smartphones. These methods and systems can use a thermal Reduced Order Model (ROM) that is trained through machine learning to provide efficient systems that can significantly reduce the time and computational resources required to build a simulation of a device's thermal behavior. The thermal model can be used in different usage scenarios with different power management and thermal management controls to test the device's thermal behavior.

Abstract: Machine assisted system and method for performing dynamic thermal management (DTM) analysis are described. In one embodiment, the method can include receiving a power profile associated with IP blocks in an integrated circuit (IC) system modeled by a Krylov reduced order model (ROM). The power profile can represent power consumption of each of the blocks based on a predefined operating scenario. The method can additionally include evaluating the temperature of each of the blocks of the IC system for the current time step based on the power profile and the Krylov ROM. The method can further include calculating new power values based on the current temperature field of each of the blocks of the IC system, wherein the power profile can be updated with the new power value for the temperature of each of the blocks of the IC system for the next time step.

Abstract: Machine assisted system and method for performing dynamic thermal management (DTM) analysis are described. In one embodiment, the method can include receiving a power profile associated with IP blocks in an integrated circuit (IC) system modeled by a Krylov reduced order model (ROM). The power profile can represent power consumption of each of the blocks based on a predefined operating scenario. The method can additionally include evaluating the temperature of each of the blocks of the IC system for the current time step based on the power profile and the Krylov ROM. The method can further include calculating new power values based on the current temperature field of each of the blocks of the IC system, wherein the power profile can be updated with the new power value for the temperature of each of the blocks of the IC system for the next time step.

Abstract: Methods and systems are described that improve simulations which use thermal models to test dynamic thermal mitigation of devices, such as smartphones. These methods and systems can use a thermal Reduced Order Model (ROM) that is trained through machine learning to provide efficient systems that can significantly reduce the time and computational resources required to build a simulation of a device's thermal behavior. The thermal model can be used in different usage scenarios with different power management and thermal management controls to test the device's thermal behavior.