FORECASTER: A Continual Lifelong Learning Approach to Improve Hardware Efficiency

Computer applications are continuously evolving. However, significant knowledge can be harvested from older applications or versions and applied in the context of newer applications or versions. Such a vision can be realized with Continual Lifelong Learning. Therefore, we propose to employ continual lifelong learning to dynamically tune hardware configurations based on application behavior. The goal of such tuning is to maximize hardware efficiency (i.e., maximize an application performance while minimizing the hardware energy consumption). Our proposed approach, FORECASTER, uses deep reinforcement learning to continually learn during the execution of an application as well as propagate and utilize the accumulated knowledge during subsequent executions of the same or new application. We propose a novel hardware and ISA support to implement deep reinforcement learning. We implement FORECASTER and compare its performance against prior learning-based hardware reconfiguration approaches. Our results show that FORECASTER can save an average 16% of system power over the baseline setup with full usage of hardware while sacrificing an average of 4.7% of execution time.