SYSTEM AND METHOD FOR MULTI-CORRELATIVE LEARNING THERMAL MANAGEMENT OF A SYSTEM ON A CHIP IN A PORTABLE COMPUTING DEVICE

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.