07041 Abstracts Collection -- Power-aware Computing Systems

From January 21, 2007 to January 26, 2007, the Dagstuhl Seminar 07041``Power-aware Computing Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and discussed ongoing work and open problems. This report compiles abstracts of the seminar presentations as well as the seminar results and ideas, providing hyperlinks to full papers wherever possible

Energy-efficient coordinated electric truck-drone hybrid delivery service planning

Recent works have shown that a coordinated delivery strategy in which a drone collaborates with a truck using it as a moving depot is quite effective in improving the performance and energy efficiency of the delivery process. As most of these works come from the research community of logistics and transportation, they are instead focused on the optimality of the algorithms, and neglect two critical issues: (1) they consider only a planar version of the problem ignoring the geographic information along the delivery route, and (2) they use a simplified battery model, truck, and drone power consumption model as they are mostly focused on optimizing delivery time alone rather than energy efficiency.In this work, we propose a greedy heuristic algorithm to deter-mine the most energy-efficient sequence of deliveries in which a drone and an EV truck collaborate in the delivery process, while accounting for the two above aspects. In our scenario, a drone delivers packages starting from the truck and returns to the truck after the delivery, while the truck continues on its route and possibly delivers other packages. Results show that, by carefully using the drone’s energy along the truck delivery route, we can achieve 43-69% saving of the truck battery energy on average over a set of different delivery sets and different drone battery sizes. We also compared two "common-sense" heuristics, concerning which we saved up to 42%

Battery-aware electric truck delivery route exploration

The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which uses a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on the EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study

SystemC-AMS Simulation of Energy Management of Electric Vehicles

Electric vehicles (EV) are rapidly invading the market, since they are clean, quiet and energy efficient. However, there are many factors that discourage EVs for current and potential customers. Among them, driving range is one of the most critical issues: running out of battery charge while driving results in serious inconvenience even comparable to vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. As a result, the dimensioning of the energy subsystem of an EV is a crucial activity. The choice of the power components and of the adopted policies should thus be validated at design time through simulations, that estimate the vehicle driving range under reference driving profiles. It is thus necessary to build a simulation framework that takes into account an EV power consumption model, dependent on the characteristics of the vehicle and of the driving route, plus accurate models for all power components, including batteries and green power sources. The goal of this paper is to achieve early EV simulation, so that the designer can estimate at design time the driving range of the vehicle, validate the adopted components and policies and evaluate alternative configurations

A SystemC-AMS Framework for the Design and Simulation of Energy Management in Electric Vehicles

Driving range is one of the most critical issues for electric vehicles (EVs): running out of battery charge while driving results in serious inconvenience even comparable to a vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. This may discourage EVs for current and potential customers. As an effect, the dimensioning of the energy subsystem of an EV is a crucial issue: the choice of the energy storage components and the policies for their management should be validated at design time through simulations, so to estimate the vehicle driving range under reference driving profiles. Thus, it is necessary to build a simulation framework that considers an EV power consumption model that accounts for the characteristics of the vehicle and the driving route, plus accurate models for all power components, including batteries and renewable power sources. The goal of this paper is to achieve such an early EV simulation, through the definition of a SystemC-AMS framework, which models simultaneously the physical and mechanical evolution, together with energy flows and environmental characteristics. The proposed solution extends the state-of-the-art framework for the simulation of electrical energy systems with support for mechanical descriptions and the AC domain, by finding a good balance between accuracy and simulation speed and by formalizing the new information and energy flows. The experimental results demonstrate that the performance of the proposed approach in terms of accuracy and simulation speed w.r.t. the current state-of-the-art and its effectiveness at supporting EV design with an enhanced exploration of the alternatives

Memory-aware energy-optimal frequency assignment for dynamic supply voltage scaling

Dynamic supply voltage scaling (DVS) is one of the best ways to reduce the energy consumption of a device when there is a super-linear relationship between energy and supply voltage, and a pseudo-linear relationship between delay and supply voltage. However, most DVS schemes scale the clock frequency of the supply-voltage-clock-scalable (SVCS) CPU only and do not address the energy consumption of the memory. The memory is generally non-supply-voltage-scalable (NSVS), but its energy consumption is variable to its clock frequency and the total execution time. Thus, DVS for an SVCS CPU cannot achieve an optimal system-wide energy saving without consideration of the memory, as far as it is controlled by an SVCS CPU. We introduce an energy-optimal frequency assignment, for both an SVCS CPU and a synchronous NSVS memory, which optimizes the system-wide energy consumption. We derive the energy-optimal clock frequencies for an SVCS CPU and a synchronous NSVS memory, as a function of the number of processor clock cycles, the number of memory accesses and the hardware energy model. Our technique modifies the frequency assignment of the CPU and the memory used in previous DVS schemes, which ignore the memory energy. It enables the system-wide energyoptimal settings and achieves additional 50 % energy reduction over previous DVS schemes. This technique can also be applicable to synchronous NSVS peripheral devices

Embedded system hardware design course track for CS students

Qualified software engineers are often in charge of system architecture design, system software design and many hardware-related issues, especially for embedded systems. Nowadays, embedded systems are equipped with fully-functional operating systems, multi-media applications, communication protocols, and so on. Since the portion of software is getting larger and larger than hardware, it is natural that software engineers are more promising in management of system-level design and integration. To supply qualified software engineers, the School of Computer Science and Engineering in Seoul National University offers a series of hardware design courses on embedded systems. They consist of FPGA design, board-level hardware design, microprocessor-based embedded system and system software design. Actual prototype implementations are mandatory in each course. The track ends up with a two-semester design project. The course track produces 20 to 30 CS-background students with intensive experience of hardware design and implementation every year. This paper introduces the outline of the course track and results. 1

Bus Encoding for Low-Power High-Performance Memory Systems

High-performance memory buses consume large energy as they include termination networks, BiCMOS and/or open-drain output. This paper introduces power reduction techniques for memory systems deliberating on burst-mode transfers over the high-speed bus specifications such as Low Voltage BiCMOS (LVT), Gunning Transfer Logic (GTL+) and Stub Series Termination Logic (SSTL 2) which are widely used. The reduction techniques take both the static and the dynamic power consumption into account because most high-performance bus drivers and end-termination networks dissipate significant static power as well. Extensive performance analysis is conducted through mathematical analysis and trace datadriven simulations. We had reduction of 14% with random data and up to 67.5% with trace data