Deep learning

Evolutionary biology

Biology

Reinforcement learning

Operating system

Distributed computing

Computer architecture

Function (biology)

Parallel computing

Embedded system

Artificial intelligence

Composite material

Mobile device

Materials science

Programming language

Range (aeronautics)

Computer science

Oracle

State (computer science)

Multiprocessing

IEEE Computer Architecture Letters

Heterogeneous multiprocessor system-on-chips (SoCs) provide a wide range of parameters that can be managed dynamically. For example, one can control the type (big/little), number and frequency of active cores in state-of-the-art mobile processors at runtime. These runtime choices lead to more than 10× range in execution time, 5× range in power consumption, and 50× range in performance per watt. Therefore, it is crucial to make optimum power management decisions as a function of dynamically varying workloads at runtime. This paper presents a reinforcement learning approach for dynamically controlling the number and frequency of active big and little cores in mobile processors. We propose an efficient deep Q-learning methodology to optimize the performance per watt (PPW). Experiments using Odroid XU3 mobile platform show that the PPW achieved by the proposed approach is within 1 percent of the optimal value obtained by an oracle.

A Deep Q-Learning Approach for Dynamic Management of Heterogeneous Processors