Marcus T. Schmitz

Considering power variations of DVS processing elements for energy minimisation in distributed systems

Dynamic voltage scaling (DVS) is a powerful technique to reduce power dissipation in embedded systems. In this paper we investigate the problem of considering DVS-processing element (DVS-PE) power variations dependent on the executed tasks, during the synthesis of distributed embedded systems, and its impact on the energy savings. Unlike previous approaches, which minimise the energy consumption by exploiting the available slack time without considering the PE power profiles, a new and fast heuristic for the voltage scaling problem is proposed, which improves the voltage selection for each task dependent on the individual power dissipation caused by that task. Experimental results show that energy reductions with up to 80.7% were achieved by integrating the proposed DVS algorithm, which considers the PE power profiles, into the co-synthesis of distributed systems.

Overhead-conscious voltage selection for dynamic and leakage energy reduction of time-constrained systems

Dynamic voltage scaling and adaptive body biasing have been shown to reduce dynamic and leakage power consumption effectively. In this paper, we optimally solve the combined supply voltage and body bias selection problem for multi-processor systems with imposed time constraints, explicitly taking into account the transition overheads implied by changing voltage levels. Both energy and time overheads are considered. We investigate the continuous voltage scaling as well as its discrete counterpart, and we prove NP-hardness in the discrete case. Furthermore, the continuous voltage scaling problem is formulated and solved using nonlinear programming with polynomial time complexity, while for the discrete problem we use mixed integer linear programming. Extensive experiments, conducted on several benchmarks and a real-life example, are used to validate the approaches.

Improving routing efficiency for network-on-chip through contention-aware input selection

The performance of network-on-chip (NoC) largely depends on the underlying routing techniques, which have two constituencies: output selection and input selection. Previous research on routing techniques for NoC has focused on the improvement of output selection. This paper investigates the impact of input selection, and presents a novel contention-aware input selection (CAIS) technique for NoC that improves the routing efficiency. When there are contentions of multiple input channels competing for the same output channel, CAIS decides which input channel obtains the access depending on the contention level of the upstream switches, which in turn removes possible network congestion. Simulation results with different synthetic and real-life traffic patterns show that, when combined with either deterministic or adaptive output selection, CAIS achieves significant better performance than the traditional first-come-first-served (FCFS) input selection, with low hardware overhead (<3%)

Combined time and information redundancy for SEU-tolerance in energy-efficient real-time systems

Recently, the tradeoff between energy consumption and fault-tolerance in real-time systems has been highlighted. These works have focused on dynamic voltage scaling (DVS) to reduce dynamic energy dissipation and on-time redundancy to achieve transient-fault tolerance. While the time redundancy technique exploits the available slack-time to increase the fault-tolerance by performing recovery executions, DVS exploits slack-time to save energy. Therefore, we believe there is a resource conflict between the time-redundancy technique and DVS. The first aim of this paper is to propose the use of information redundancy to solve this problem. We demonstrate through analytical and experimental studies that it is possible to achieve both higher transient fault-tolerance [tolerance to single event upsets (SEUs)] and less energy using a combination of information and time redundancy when compared with using time redundancy alone. The second aim of this paper is to analyze the interplay of transient-fault tolerance (SEU-tolerance) and adaptive body biasing (ABB) used to reduce static leakage energy, which has not been addressed in previous studies. We show that the same technique (i.e., the combination of time and information redundancy) is applicable to ABB-enabled systems and provides more advantages than time redundancy alone.

Energy Optimization of Multiprocessor Systems on Chip by Voltage Selection

Dynamic voltage selection and adaptive body biasing have been shown to reduce dynamic and leakage power consumption effectively. In this paper, we optimally solve the combined supply voltage and body bias selection problem for multiprocessor systems with imposed time constraints, explicitly taking into account the transition overheads implied by changing voltage levels. Both energy and time overheads are considered. The voltage selection technique achieves energy efficiency by simultaneously scaling the supply and body bias voltages in the case of processors and buses with repeaters, while energy efficiency on fat wires is achieved through dynamic voltage swing scaling. We investigate the continuous voltage selection as well as its discrete counterpart, and we prove strong NP-hardness in the discrete case. Furthermore, the continuous voltage selection problem is solved using nonlinear programming with polynomial time complexity, while for the discrete problem, we use mixed integer linear programming and a polynomial time heuristic. We propose an approach that combines voltage selection and processor shutdown in order to optimize the total energy

Simultaneous communication and processor voltage scaling for dynamic and leakage energy reduction in time-constrained systems

We propose a new technique for the combined voltage scaling of processors and communication links, taking into account dynamic as well as leakage power consumption. The voltage scaling technique achieves energy efficiency by simultaneously scaling the supply and body bias voltages in the case of processors and buses with repeaters, while energy efficiency on fat wires is achieved through dynamic voltage swing scaling. We also introduce a set of accurate communication models for the energy estimation of voltage scalable embedded systems. In particular, we demonstrate that voltage scaling of bus repeaters and dynamic adaption of the voltage swing on fat wires can significantly influence the systems energy consumption. Experimental results, conducted on numerous generated benchmarks and a real-life example, demonstrate that substantial energy savings can be achieved with the proposed techniques.

Quasi-Static Voltage Scaling for Energy Minimization with Time Constraints

Supply voltage scaling and adaptive body-biasing are important techniques that help to reduce the energy dissipation of embedded systems. This is achieved by dynamically adjusting the voltage and performance settings according to the application needs. In order to take full advantage of slack that arises from variations in the execution time, it is important to recalculate the voltage (performance) settings during run time, i.e., online. However voltage scaling (VS) is computationally expensive, and thus significantly hampers the possible energy savings. To overcome the online complexity, we propose a quasi-static voltage scaling scheme, with a constant online time complexity O(1). This allows us to increase the exploitable slack as well as to avoid the energy dissipated due to online recalculation of the voltage settings. We conduct several experiments that demonstrate the advantages of the proposed technique over the previously published voltage scaling approaches.

Cache size selection for performance, energy and reliability of time-constrained systems

Improving performance, reducing energy consumption and enhancing reliability are three important objectives for embedded computing systems design. In this paper, we study the joint impact of cache size selection on these three objectives. For this purpose, we conduct extensive fault injection experiments on five benchmark examples using a cycle-accurate processor simulator. Performance and reliability are analyzed using the performability metric. Overall, our experiments demonstrate the importance of a careful cache size selection when designing energy-efficient and reliable systems. Furthermore, the experimental results show the existence of optimal or Pareto-optimal cache size selection to optimize the three design objectives

Cosynthesis of energy-efficient multimode embedded systems with consideration of mode-execution probabilities

We present a novel co-design methodology for the synthesis of energy-efficient embedded systems. In particular, we concentrate on distributed embedded systems that accommodate several different applications within a single device, i.e., multimode embedded systems. Based on the key observation that operational modes are executed with different probabilities, that is, the system spends uneven amounts of time in the different modes, we develop a new co-design technique that exploits this property to significantly reduce energy dissipation. Energy and cost savings are achieved through a suitable synthesis process that yields better hardware-resource-sharing opportunities. We conduct several experiments, including a realistic smart phone example, that demonstrate the effectiveness of our approach. Reductions in power consumption of up to 64% are reported.

Workload-ahead-driven online energy minimization techniques for battery-powered embedded systems with time-constraints

This article proposes a new online voltage scaling (VS) technique for battery-powered embedded systems with real-time constraints. The VS technique takes into account the execution times and discharge currents of tasks to further reduce the battery charge consumption when compared to the recently reported slack forwarding technique [Ahmed and Chakrabarti 2004], while maintaining low online complexity of O(1). Furthermore, we investigate the impact of online rescheduling and remapping on the battery charge consumption for tasks with data dependency which has not been explicitly addressed in the literature and propose a novel rescheduling/remapping technique. Finally, we take leakage power into consideration and extend the proposed online techniques to include adaptive body biasing (ABB) which is used to reduce the leakage power. We demonstrate and compare the efficiency of the presented techniques using seven real-life benchmarks and numerous automatically generated examples.