Abstract: Provided herein are dynamic adaptive scheduling (DAS) systems. In some embodiments, the DAS systems include a first scheduler, a second scheduler that is slower than the first scheduler, and a runtime preselection classifier that is operably connected to the first scheduler and the second scheduler, which runtime preselection classifier is configured to effect selective use of the first scheduler or the second scheduler to perform a given scheduling task. Related systems, computer readable media, and additional methods are also provided.

Abstract: A user-space emulation framework for heterogeneous system-on-chip (SoC) design is provided. Embodiments described herein propose a portable, Linux-based emulation framework to provide an ecosystem for hardware-software co-design of heterogenous SoCs (e.g., domain-specific SoCs (DSSoCs)) and enable their rapid evaluation during the pre-silicon design phase. This framework holistically targets three key challenges of heterogeneous SoC design: accelerator integration, resource management, and application development. These challenges are addressed via a flexible and lightweight user-space runtime environment that enables easy integration of new accelerators, scheduling heuristics, and user applications, and the utility of each is illustrated through various case studies. A prototype compilation toolchain is introduced that enables automatic mapping of unlabeled C code to heterogeneous SoC platforms.

Abstract: Runtime task scheduling using imitation learning (IL) for heterogenous many-core systems is provided. Domain-specific systems-on-chip (DSSoCs) are recognized as a key approach to narrow down the performance and energy-efficiency gap between custom hardware accelerators and programmable processors. Reaching the full potential of these architectures depends critically on optimally scheduling the applications to available resources at runtime. Existing optimization-based techniques cannot achieve this objective at runtime due to the combinatorial nature of the task scheduling problem. In an exemplary aspect described herein, scheduling is posed as a classification problem, and embodiments propose a hierarchical IL-based scheduler that learns from an Oracle to maximize the performance of multiple domain-specific applications. Extensive evaluations show that the proposed IL-based scheduler approximates an offline Oracle policy with more than 99% accuracy for performance- and energy-based optimization objectives.

Abstract: Hierarchical and lightweight imitation learning (IL) for power management of embedded systems-on-chip (SoCs), also referred to herein as HiLITE, is provided. Modern SoCs use dynamic power management (DPM) techniques to improve energy efficiency. However, existing techniques are unable to efficiently adapt the mntime decisions considering multiple objectives (e.g., energy and real-time requirements) simultaneously on heterogeneous platforms. To address this need, embodiments described herein propose HiLITE, a hierarchical IL framework that maximizes energy efficiency while satisfying soft real-time constraints on embedded SoCs. This approach first trains DPM policies using IL; then, it applies a regression policy at runtime to minimize deadline misses. HiLITE improves the energy-delay product by 40% on average, and reduces deadline misses by up to 76%, compared to state-of-the-art approaches.

Abstract: In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system.

Abstract: In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system.

Abstract: In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system.

Abstract: Method and system for non-linear modeling of physiological behavior, such as R-R intervals, in implantable devices, such as a rate responsive pacemakers, comprising a comprehensive modeling and optimization methodology based on fractional calculus and constrained finite horizon optimal control theory that allows for allows for fine-grain optimization of pacemaker response to heart rate variations; and the theoretical basis on which a hardware implementation of the fractional optimal controller that can respond to changes in the heart rate dynamics. Present invention describes a fractal approach to pacemaker control based on the constrained finite horizon optimal control problem. This is achieved by modeling the heart rate dynamics via fractional differential equations. Also, by using calculus of variations, the invention describes how the constrained finite horizon optimal control problem can be reduced to solving a linear system of equations.

Abstract: Methods of determining a therapy for inhibiting growth or pathogenesis of one or more cell colonies based on modeling intracellular and/or intercellular communication mechanisms utilized by the cell type(s) in question, selective pressures on the one or more cell colonies within a cell population, and therapies or therapeutic agents available to a user. A dynamic molecular-level model of a cell population representing one or more cell colonies models differing aspects of one or more resistance forming mechanisms and the effects that the available treatment agents or therapies may have on the differing aspects. The dynamic molecular-model, in combination with a selective pressure model, formulates an optimization problem that is solved to determine amounts of the differing treatment agents or therapies.

Abstract: Method and system for non-linear modeling of physiological behavior, such as R-R intervals, in implantable devices, such as a rate responsive pacemakers, comprising a comprehensive modeling and optimization methodology based on fractional calculus and constrained finite horizon optimal control theory that allows for allows for fine-grain optimization of pacemaker response to heart rate variations; and the theoretical basis on which a hardware implementation of the fractional optimal controller that can respond to changes in the heart rate dynamics. Present invention describes a fractal approach to pacemaker control based on the constrained finite horizon optimal control problem. This is achieved by modeling the heart rate dynamics via fractional differential equations. Also, by using calculus of variations, the invention describes how the constrained finite horizon optimal control problem can be reduced to solving a linear system of equations.