摘要:
Various embodiments of methods and systems for multi-correlative learning thermal management (“MLTM”) techniques implemented in a portable computing device (“PCD”) are disclosed. Notably, in many PCDs, thermal energy levels measured by individual temperature sensors in the PCD may be attributable to a plurality of processing components, i.e. thermal aggressors. Generally, as more power is consumed by the thermal aggressors, the resulting generation of thermal energy may cause the temperature thresholds associated with temperature sensors located around the chip to be exceeded, thereby necessitating that the performance of the PCD be sacrificed in an effort to reduce thermal energy generation. Advantageously, embodiments of MLTM systems and methods recognize that multiple thermal aggressors affect temperature readings of individual temperature sensors differently and seek to identify and apply optimum performance level settings combinations that optimize quality of service (“QoS”) while maintaining thermal energy levels at the sensors within predetermined temperature thresholds.
摘要:
A temperature of a component within the portable computing device (PCD) may be monitored along with a parameter associated with the temperature. The parameter associated with temperature may be an operating frequency, transmission power, or a data flow rate. It is determined if the temperature has exceeded a threshold value. If the temperature has exceeded the threshold value, then the temperature is compared with a temperature set point and a first error value is then calculated based on the comparison. Next, a first optimum value of the parameter is determined based on the first error value. If the temperature is below or equal to the threshold value, then a present value of the parameter is compared with a desired threshold for the parameter and a second error value is calculated based on the comparison. A second optimum value of the parameter may be determined based on the second error value.