Dynamic Resource Management of Heterogeneous Mobile Platforms via Imitation Learning

Sumit K. Mandal, Ganapati Bhat, Chetan Arvind Patil, Janardhan Rao Doppa, Partha Pratim Pande, Umit Y. Ogras

Research output: Contribution to journalArticlepeer-review

40 Scopus citations


The complexity of heterogeneous mobile platforms is growing at a rate faster than our ability to manage them optimally at runtime. For example, state-of-the-art systems-on-chip (SoCs) enable controlling the type (Big/Little), number, and frequency of active cores. Managing these platforms becomes challenging with the increase in the type, number, and supported frequency levels of the cores. However, existing solutions used in mobile platforms still rely on simple heuristics based on the utilization of cores. This paper presents a novel and practical imitation learning (IL) framework for dynamically controlling the type (Big/Little), number, and the frequencies of active cores in heterogeneous mobile processors. We present efficient approaches for constructing an Oracle policy to optimize different objective functions, such as energy and performance per Watt (PPW). The Oracle policies enable us to design low-overhead power management policies that achieve near-optimal performance matching the Oracle. Experiments on a commercial platform with 19 benchmarks show on an average 101% PPW improvement compared to the default interactive governor.

Original languageEnglish (US)
Article number8770273
Pages (from-to)2842-2854
Number of pages13
JournalIEEE Transactions on Very Large Scale Integration (VLSI) Systems
Issue number12
StatePublished - Dec 2019


  • Heterogeneous computing
  • imitation learning (IL)
  • multi-processor systems-on-chip (SoCs)
  • multicore architectures

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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