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.

Safe navigation is a fundamental challenge in multi-robot systems due to the uncertainty surrounding the future trajectory of the robots that act as obstacles for each other. In this work, we propose a principled data-driven approach where each robot repeatedly solves a finite horizon optimization problem subject to collision avoidance constraints with latter being formulated as distributionally robust conditional value-at-risk (CVaR) of the distance between the agent and a polyhedral obstacle geometry. Specifically, the CVaR constraints are required to hold for all distributions that are close to the empirical distribution constructed from observed samples of prediction error collected during execution. The generality of the approach allows us to robustify against prediction errors that arise under commonly imposed assumptions in both distributed and decentralized settings. We derive tractable finite-dimensional approximations of this class of constraints by leveraging convex and minmax duality results for Wasserstein distributionally robust optimization problems. The effectiveness of the proposed approach is illustrated in a multi-drone navigation setting implemented in Gazebo platform.

Current research on energy related problems such as eco-routing, eco-driving and range prediction for electric vehicles (EVs) primarily considers the effect of longitudinal dynamics on EV energy consumption. However, real-world driving includes longitudinal as well as lateral motion. Therefore, it is important to understand the effects of lateral dynamics on battery energy consumption. This paper conducts an analysis of the stated effect and validates its significance through simulations. Specifically, this study demonstrates that inclusion of the effect of lateral dynamics can improve accuracy and reliability of solutions in eco-routing, eco-driving and range prediction applications.

Accelerated gradient methods are the cornerstones of large-scale, data-driven optimization problems that arise naturally in machine learning and other fields concerning data analysis. We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time stability of dynamical systems. The method presents itself as a generalization of simple gradient-based methods suitably scaled to achieve convergence to the optimizer in a fixed-time, independent of the initialization. We achieve this by first leveraging a continuous-time framework for designing fixed-time stable dynamical systems, and later providing a consistent discretization strategy, such that the equivalent discrete-time algorithm tracks the optimizer in a practically fixed number of iterations. We also provide a theoretical analysis of the convergence behavior of the proposed gradient flows, and their robustness to additive disturbances for a range of functions obeying strong convexity, strict convexity, and possibly nonconvexity but satisfying the Polyak-Łojasiewicz inequality. We also show that the regret bound on the convergence rate is constant by virtue of the fixed-time convergence. The hyperparameters have intuitive interpretations and can be tuned to fit the requirements on the desired convergence rates. We validate the accelerated convergence properties of the proposed schemes on a range of numerical examples against the state-of-the-art optimization algorithms. Our work provides insights on developing novel optimization algorithms via discretization of continuous-time flows.

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.

The first rigorous theoretical analysis (Horoba, Sudholt (GECCO 2010)) of an ant colony optimizer for the stochastic shortest path problem suggests that ant system experience significant difficulties when the input data is prone to noise. In this work, we propose a slightly different ant optimizer to deal with noise.