Resumen: Cache working-set adaptation is key as embedded systems move to multiprocessor and Simultaneous Multithreaded Architectures (SMT) because interthread pollution harms system performance and battery life. Light-Power NUCA (LP-NUCA) is a working-set adaptive cache that depends on temporal-locality to save energy. This work identifies the sources of energy waste in LP-NUCAs: parallel access to the tag and data arrays of the tiles and low locality phases with useless block migration. To counteract both issues, we prove that switching to serial access reduces energy without harming performance and propose a machine learning Adaptive Drop Rate (ADR) controller that minimizes the amount of replacement and migration when locality is low.