13 research outputs found
Adaptive Knobs for Resource Efficient Computing
Performance demands of emerging domains such as artificial intelligence, machine learning and vision, Internet-of-things etc., continue to grow. Meeting such requirements on modern multi/many core systems with higher power densities, fixed power and energy budgets, and thermal constraints exacerbates the run-time management challenge. This leaves an open problem on extracting the required performance within the power and energy limits, while also ensuring thermal safety. Existing architectural solutions including asymmetric and heterogeneous cores and custom acceleration improve performance-per-watt in specific design time and static scenarios. However, satisfying applications’ performance requirements under dynamic and unknown workload scenarios subject to varying system dynamics of power, temperature and energy requires intelligent run-time management.
Adaptive strategies are necessary for maximizing resource efficiency, considering i) diverse requirements and characteristics of concurrent applications, ii) dynamic workload variation, iii) core-level heterogeneity and iv) power, thermal and energy constraints. This dissertation proposes such adaptive techniques for efficient run-time resource management to maximize performance within fixed budgets under unknown and dynamic workload scenarios. Resource management strategies proposed in this dissertation comprehensively consider application and workload characteristics and variable effect of power actuation on performance for pro-active and appropriate allocation decisions. Specific contributions include i) run-time mapping approach to improve power budgets for higher throughput, ii) thermal aware performance boosting for efficient utilization of power budget and higher performance, iii) approximation as a run-time knob exploiting accuracy performance trade-offs for maximizing performance under power caps at minimal loss of accuracy and iv) co-ordinated approximation for heterogeneous systems
through joint actuation of dynamic approximation and power knobs for performance guarantees with minimal power consumption.
The approaches presented in this dissertation focus on adapting existing mapping techniques, performance boosting strategies, software and dynamic approximations to meet the performance requirements, simultaneously considering system constraints. The proposed strategies are compared against relevant state-of-the-art run-time management frameworks to qualitatively evaluate their efficacy
Concurrent Application Bias Scheduling for Energy Efficiency of Heterogeneous Multi-Core platforms
Minimizing energy consumption of concurrent applications on
heterogeneous multi-core platforms is challenging given the diversity in
energy-performance profiles of both the applications and hardware.
Adaptive learning techniques made the exhaustive Pareto-optimal space
exploration practically feasible to identify an energy-efficient
configuration. The existing approaches consider a single application's
characteristic for optimizing energy consumption. However, an optimal
configuration for a given single application may not be optimal when a
new application arrives. Although some related works do consider
concurrent applications scenarios, these approaches overlook the weight
of total energy consumption per application, restricting those from
prioritizing among applications. We address this limitation by
considering the mutual effect of concurrent applications on system-wide
energy consumption to adapt resource configuration at run-time. We
characterize each application's power-performance profile as a weighted
bias through off-line profiling. We infer this model combined with an
on-line predictive strategy to make resource allocation decisions for
minimizing energy consumption while honoring performance requirements.
The proposed strategy is implemented as a user-space process and
evaluated on a heterogeneous hardware platform of Odroid XU3 over the
Rodinia benchmark suite. Experimental results show up to 61% of energy
saving compared to the standard baseline of Linux governors and up to
27% of energy gain compared to state-of-the-art adaptive learning-based
resource management techniques.</p
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital
healthcare services to clients through remote sensing, continuous monitoring,
and data analytics. Smart eHealth applications sense input data from multiple
modalities, transmit the data to edge and/or cloud nodes, and process the data
with compute intensive machine learning (ML) algorithms. Run-time variations
with continuous stream of noisy input data, unreliable network connection,
computational requirements of ML algorithms, and choice of compute placement
among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth
applications. In this chapter, we present edge-centric techniques for optimized
compute placement, exploration of accuracy-performance trade-offs, and
cross-layered sense-compute co-optimization for ML-driven eHealth applications.
We demonstrate the practical use cases of smart eHealth applications in
everyday settings, through a sensor-edge-cloud framework for an objective pain
assessment case study
Digital Health-Enabled Community-Centered Care: Scalable Model to Empower Future Community Health Workers Using Human-in-the-Loop Artificial Intelligence
Digital health–enabled community-centered care (D-CCC) represents a pioneering vision for the future of community-centered care. D-CCC aims to support and amplify the digital footprint of community health workers through a novel artificial intelligence–enabled closed-loop digital health platform designed for, and with, community health workers. By focusing digitalization at the level of the community health worker, D-CCC enables more timely, supported, and individualized community health worker–delivered interventions. D-CCC has the potential to move community-centered care into an expanded, digitally interconnected, and collaborative community-centered health and social care ecosystem of the future, grounded within a robust and digitally empowered community health workforce.</p
Proceedings of the 2019 Design, Automation & Test in Europe (DATE)
Run-time resource allocation of heterogeneous multi-core systems is challenging with varying workloads and limited power and energy budgets. User interaction within these systems changes the performance requirements, often conflicting with concurrent applications' objective and system constraints. Current resource allocation approaches focus on optimizing fixed objective, ignoring the variation in system and applications' objective at run-time. For an efficient resource allocation, the system has to operate autonomously by formulating a hierarchy of goals. We present goal-driven autonomy (GDA) for on-chip resource allocation decisions, which allows systems to generate and prioritize goals in response to the workload and system dynamic variation. We implemented a proof-of-concept resource management framework that integrates the proposed goal management control to meet power, performance and user requirements simultaneously. Experimental results on an Exynos platform containing ARM's big. LITTLE-based heterogeneous multi-processor (HMP) show the effectiveness of GDA in efficient resource allocation in comparison with existing fixed objective policies
Approximation-aware coordinated power/performance management for heterogeneous multi-cores
Run-time resource management of heterogeneous multi-core systems is challenging due to i) dynamic workloads, that often result in ii) conflicting knob actuation decisions, which potentially iii) compromise on performance for thermal safety. We present a runtime resource management strategy for performance guarantees under power constraints using functionally approximate kernels that exploit accuracy-performance trade-offs within error resilient applications. Our controller integrates approximation with power knobs-DVFS, CPU quota, task migration-in coordinated manner to make performance-aware decisions on power management under variable workloads. Experimental results on Odroid XU3 show the effectiveness of this strategy in meeting performance requirements without power violations compared to existing solutions
Digital Health-Enabled Community-Centered Care: Scalable Model to Empower Future Community Health Workers Using Human-in-the-Loop Artificial Intelligence.
Digital health-enabled community-centered care (D-CCC) represents a pioneering vision for the future of community-centered care. D-CCC aims to support and amplify the digital footprint of community health workers through a novel artificial intelligence-enabled closed-loop digital health platform designed for, and with, community health workers. By focusing digitalization at the level of the community health worker, D-CCC enables more timely, supported, and individualized community health worker-delivered interventions. D-CCC has the potential to move community-centered care into an expanded, digitally interconnected, and collaborative community-centered health and social care ecosystem of the future, grounded within a robust and digitally empowered community health workforce
2020 33rd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID)
Modern battery powered Embedded Systems (ES)
must provide a high performance with minimal energy consumption to enhance the user experience. However, these two are
often conflicting objectives. In current ES resource management
techniques, user behavior and preferences are only indirectly
or not at all considered. In this paper, we present a novel
user- and battery-aware resource management framework for
multi-processor architectures that considers these conflicting
requirements and dynamic unknown workloads at run-time to
maximize user satisfaction. Proposed technique learns user’s
habits to dynamically adjust the resource management schemes
based on the data it collects regarding user’s plug-in behavior,
battery charge status, and workloads variability at run-time. This
information is used to improve the balance between performance
and energy consumption, and thus optimize the Quality of Experience (QoE). Our evaluation results show that our framework
enhances the user experience by 22% in comparison with the
existing state-of-the-art.</p