DReAM: An approach to estimate per-Task DRAM energy in multicore systems

Accurate per-task energy estimation in multicore systems would allow performing per-task energy-aware task scheduling and energy-aware billing in data centers, among other applications. Per-task energy estimation is challenged by the interaction between tasks in shared resources, which impacts tasks’ energy consumption in uncontrolled ways. Some accurate mechanisms have been devised recently to estimate per-task energy consumed on-chip in multicores, but there is a lack of such mechanisms for DRAM memories. This article makes the case for accurate per-task DRAM energy metering in multicores, which opens new paths to energy/performance optimizations. In particular, the contributions of this article are (i) an ideal per-task energy metering model for DRAM memories; (ii) DReAM, an accurate yet low cost implementation of the ideal model (less than 5% accuracy error when 16 tasks share memory); and (iii) a comparison with standard methods (even distribution and access-count based) proving that DReAM is much more accurate than these other methods.Peer ReviewedPostprint (author's final draft

Smart management energy systems in industry 4.0

In its origins, the term Industry 4.0 was associated with the computerization of manufacturing and with the diffusion of new network technologies in order to improve the communication paradigm. Today, the definition and implementation of Industry 4.0 include a number of trends, such as the Internet of Things (IoT), digital manufacturing and cyber-physical systems. Among these key elements, energy aware systems in factory automation are emerging as a challenging trend of Industry 4.0. Technological advancements in the ability to collect, transfer and analyze data by using smart energy aware systems are at the aim of this trend. Smart solutions in order to limit the power consumption of manufacturing line aim at developing and integrating new technologies and methods into smart factories in order to rapidly adapt and respond to changes in the markets’ demands for high-quality products. In fact, smart energy aware systems in factories lie at the core of both Industry 4.0 and smart manufacturing. A variety of recent advanced technologies and approaches play important roles, by exploiting innovative technologies and solutions and/or optimization methods. They allow higher levels of adaptively and flexibility in energy aware systems

Energy-aware MPC co-design for DC-DC converters

In this paper, we propose an integrated controller design methodology for the implementation of an energy-aware explicit model predictive control (MPC) algorithms, illustrat- ing the method on a DC-DC converter model. The power consumption of control algorithms is becoming increasingly important for low-power embedded systems, especially where complex digital control techniques, like MPC, are used. For DC-DC converters, digital control provides better regulation, but also higher energy consumption compared to standard analog methods. To overcome the limitation in energy efficiency, instead of addressing the problem by implementing sub-optimal MPC schemes, the closed-loop performance and the control algorithm power consumption are minimized in a joint cost function, allowing us to keep the controller power efficiency closer to an analog approach while maintaining closed-loop op- timality. A case study for an implementation in reconfigurable hardware shows how a designer can optimally trade closed-loop performance with hardware implementation performance

RootJS: Node.js Bindings for ROOT 6

We present rootJS, an interface making it possible to seamlessly integrate ROOT 6 into applications written for Node.js, the JavaScript runtime platform increasingly commonly used to create high-performance Web applications. ROOT features can be called both directly from Node.js code and by JIT-compiling C++ macros. All rootJS methods are invoked asynchronously and support callback functions, allowing non-blocking operation of Node.js applications using them. Last but not least, our bindings have been designed to platform-independent and should therefore work on all systems supporting both ROOT 6 and Node.js. Thanks to rootJS it is now possible to create ROOT-aware Web applications taking full advantage of the high performance and extensive capabilities of Node.js. Examples include platforms for the quality assurance of acquired, reconstructed or simulated data, book-keeping and e-log systems, and even Web browser-based data visualisation and analysis.Comment: 7 pages, 1 figure. To appear in the Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2016

Formal Methods for Probabilistic Energy Models

The energy consumption that arises from the utilisation of information processing systems adds a significant contribution to environmental pollution and has a big share of operation costs. This entails that we need to find ways to reduce the energy consumption of such systems. When trying to save energy it is important to ensure that the utility (e.g., user experience) of a system is not unnecessarily degraded, requiring a careful trade-off analysis between the consumed energy and the resulting utility. Therefore, research on energy efficiency has become a very active and important research topic that concerns many different scientific areas, and is as well of interest for industrial companies. The concept of quantiles is already well-known in mathematical statistics, but its benefits for the formal quantitative analysis of probabilistic systems have been noticed only recently. For instance, with the help of quantiles it is possible to reason about the minimal energy that is required to obtain a desired system behaviour in a satisfactory manner, e.g., a required user experience will be achieved with a sufficient probability. Quantiles also allow the determination of the maximal utility that can be achieved with a reasonable probability while staying within a given energy budget. As those examples illustrate important measures that are of interest when analysing energy-aware systems, it is clear that it is beneficial to extend formal analysis-methods with possibilities for the calculation of quantiles. In this monograph, we will see how we can take advantage of those quantiles as an instrument for analysing the trade-off between energy and utility in the field of probabilistic model checking. Therefore, we present algorithms for their computation over Markovian models. We will further investigate different techniques in order to improve the computational performance of implementations of those algorithms. The main feature that enables those improvements takes advantage of the specific characteristics of the linear programs that need to be solved for the computation of quantiles. Those improved algorithms have been implemented and integrated into the well-known probabilistic model checker PRISM. The performance of this implementation is then demonstrated by means of different protocols with an emphasis on the trade-off between the consumed energy and the resulting utility. Since the introduced methods are not restricted to the case of an energy-utility analysis only, the proposed framework can be used for analysing the interplay of cost and its resulting benefit in general.:1 Introduction 1.1 Related work 1.2 Contribution and outline 2 Preliminaries 3 Reward-bounded reachability properties and quantiles 3.1 Essentials 3.2 Dualities 3.3 Upper-reward bounded quantiles 3.3.1 Precomputation 3.3.2 Computation scheme 3.3.3 Qualitative quantiles 3.4 Lower-reward bounded quantiles 3.4.1 Precomputation 3.4.2 Computation scheme 3.5 Energy-utility quantiles 3.6 Quantiles under side conditions 3.6.1 Upper reward bounds 3.6.2 Lower reward bounds 3.6.2.1 Maximal reachability probabilities 3.6.2.2 Minimal reachability probabilities 3.7 Reachability quantiles and continuous time 3.7.1 Dualities 4 Expectation Quantiles 4.1 Computation scheme 4.2 Arbitrary models 4.2.1 Existential expectation quantiles 4.2.2 Universal expectation quantiles 5 Implementation 5.1 Computation optimisations 5.1.1 Back propagation 5.1.2 Reward window 5.1.3 Topological sorting of zero-reward sub-MDPs 5.1.4 Parallel computations 5.1.5 Multi-thresholds 5.1.6 Multi-state solution methods 5.1.7 Storage for integer sets 5.1.8 Elimination of zero-reward self-loops 5.2 Integration in Prism 5.2.1 Computation of reward-bounded reachability probabilities 5.2.2 Computation of quantiles in CTMCs 6 Analysed Protocols 6.1 Prism Benchmark Suite 6.1.1 Self-Stabilising Protocol 6.1.2 Leader-Election Protocol 6.1.3 Randomised Consensus Shared Coin Protocol 6.2 Energy-Aware Protocols 6.2.1 Energy-Aware Job-Scheduling Protocol 6.2.1.1 Energy-Aware Job-Scheduling Protocol with side conditions 6.2.1.2 Energy-Aware Job-Scheduling Protocol and expectation quantiles 6.2.1.3 Multiple shared resources 6.2.2 Energy-Aware Bonding Network Device (eBond) 6.2.3 HAECubie Demonstrator 6.2.3.1 Operational behaviour of the protocol 6.2.3.2 Formal analysis 7 Conclusion 7.1 Classification 7.2 Future prospects Bibliography List of Figures List of Table

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Future Greener Seaports:A Review of New Infrastructure, Challenges, and Energy Efficiency Measures

Recently, the application of renewable energy sources (RESs) for power distribution systems is growing immensely. This advancement brings several advantages, such as energy sustainability and reliability, easier maintenance, cost-effective energy sources, and ecofriendly. The application of RESs in maritime systems such as port microgrids massively improves energy efficiency and reduces the utilization of fossil fuels, which is a serious threat to the environment. Accordingly, ports are receiving several initiatives to improve their energy efficiency by deploying different types of RESs based on the power electronic converters. This paper conducts a systematic review to provide cutting-edge state-of-the-art on the modern electrification and infrastructure of seaports taking into account some challenges such as the environmental aspects, energy efficiency enhancement, renewable energy integration, and legislative and regulatory requirements. Moreover, the technological methods, including electrifications, digitalization, onshore power supply applications, and energy storage systems of ports, are addressed. Furthermore, details of some operational strategies such as energy-aware operations and peak-shaving are delivered. Besides, the infrastructure scheme to enhance the energy efficiency of modern ports, including port microgrids and seaport smart microgrids are delivered. Finally, the applications of nascent technologies in seaports are presented

CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion

Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep-learning models, and powerful hardware platforms to perceive and safely operate in real-time. However, in many contexts, some sensing modalities negatively impact perception while increasing the system's overall energy consumption. Since AS are often energy-constrained edge devices, energy-efficient sensor fusion methods have been proposed. However, existing methods either fail to adapt to changing scenario conditions or to optimize energy efficiency system-wide. We propose CARMA: a context-aware sensor fusion approach that uses context to dynamically reconfigure the computation flow on a Field-Programmable Gate Array (FPGA) at runtime. By clock-gating unused sensors and model sub-components, CARMA significantly reduces the energy used by a multi-sensory object detector without compromising performance. We use a Deep-learning Processor Unit (DPU) based reconfiguration approach to minimize the latency of model reconfiguration. We evaluate multiple context-identification strategies, propose a novel system-wide energy-performance joint optimization, and evaluate scenario-specific perception performance. Across challenging real-world sensing contexts, CARMA outperforms state-of-the-art methods with up to 1.3x speedup and 73% lower energy consumption.Comment: Accepted to be published in the 2023 ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED 2023

Experiences in Implementing an Energy-Driven Task Scheduler in RT-Linux

Dynamic voltage scaling (DVS) is being increasingly used for power management in embedded systems. Energy is a scarce resource in embedded real-time systems and energy consumption must be carefully balanced against realtime responsiveness. We describe our experiences in implementing an energy driven task scheduler in RT-Linux. We attempt to minimize the energy consumed by a taskset while guaranteeing that all task deadlines are met. Our algorithm, which we call LEDF, follows a greedy approach and schedules as many tasks as possible at a low CPU speed in a power-aware manner. We present simulation results on energy savings using LEDF, and we validate our approach using the RT-Linux testbed on the AMD Athlon 4 processor. Power measurements taken on the testbed closely match the power estimates obtained using simulation. Our results show that DVS results in significant energy savings for practical real-life task sets. We also show that when CPU speeds are restricted to only a few discrete values, this approach saves more energy than currently existing methods