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.

Abstract

Abstract-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 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. 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.

Abstract-Recently the trade-off 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 faulttolerance 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 usage 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 (SEU)) 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.

Abstract-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.

Abstract-In this paper, 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.

We present an iterative schedule optimisation for multi-rate system specifications, mapped onto heterogeneous distributed architectures containing dynamic voltage scalable processing elements (DVS-PEs). To achieve a high degree of energy reduction, we formulate a generalised DVS problem, taking into account the power variations among the executing tasks. An efficient heuristic is presented that identifies optimised supply voltages by not only "simply" exploiting slack time, but under the additional consideration of the power profiles. Thereby, this algorithm minimises the energy dissipation of heterogeneous architectures, including power managed processing elements, effectively. Further, we address the simultaneous schedule optimisation towards timing behaviour and DVS utilisation by integrating the proposed DVS heuristic into a genetic list scheduling approach. We investigate and analyse the possible energy reduction at both steps of the co-synthesis (voltage scaling and scheduling), including the power variations effects. Extensive experiments indicate that the presented work produces solutions with high quality.

Abstract