With increasing transistor density, modern heterogeneous embedded processors often exhibit high temperature gradients due to complex application scheduling scenarios which may have missed design considerations. In many use cases, off-chip ”active” cooling solutions are considered prohibitive in such reduced form factors. Core frequency throttling by existing dynamic thermal management techniques often compromises the Quality-of-Service (QoS) and violates real-time deadlines. This necessitates the adoption of intelligent resource management that simultaneously manages both thermal and latency performance. Coupled with the complexity of modern heterogeneous multi-cores, the periodic application updates that cater to ever-changing user requirements often render model-driven thermal-aware resource allocation approaches unsuitable for heterogeneous multi-core systems. For such application-architecture scenarios, we propose a novel self-learning based resource manager using Reinforcement Learning that intelligently manipulates core frequencies and task set mappings to fulfill thermal and latency objectives. Our framework employs a data-driven system modeling technique using Gaussian Process Regression to enable efficient offline training of this learning-based resource manager to avoid challenges associated with initial online training. We evaluate the approach on a heterogeneous embedded CPU-GPU platform with real workloads and observe a significant reduction in peak operating temperature when compared to the default onboard frequency governor as well as other learning-based state-of-the-art approaches.

Modern data intensive Cyber-physical Systems ubiquitously employ heterogeneous multiprocessor systems-on chips (MPSoCs) for real-time sensing, computation, and actuation. The low foot-print of such SoCs often leads to high operating temperatures beyond acceptable limits. In this context, conventional thermal management techniques such as Operating System (OS) governed frequency scaling result in drastic degradation of the quality of experience and violation of real-time requirements. In this work, we propose an analytical thermal model for heterogeneous CPU-GPU embedded platforms and demonstrate a Model Predictive Control (MPC) based scheduling strategy with a novel heuristics-based optimization technique that leverages information about future kernels to judiciously choose suitable task mapping options for minimization of the platform's peak (or maximum) temperature to prolong chip's life span while adhering to real-time performance requirements. To the best of our knowledge, this is the first work that considers future awareness along with a variety of online task mapping control actions such as partitioning, migration, and frequency tuning in the context of thermal management in heterogeneous CPU-GPU embedded platforms. We evaluate the proposed heterogeneous framework on an Odroid-XU4 board using OpenCL based workloads and demonstrate its effectiveness in reducing the platform peak temperature.