In response to the tremendous growth of the Internet, towards what we call the Internet of Things (IoT), there is a need to move from costly, high-time-to-market specific-purpose hardware to flexible, low-time-to-market general-purpose devices for packet processing. Among several such devices, GPUs have attracted attention in the past, mainly because the high computing demand of packet processing applications can, potentially, be satisfied by these throughput-oriented machines. However, another important aspect of such applications is the packet latency which, if not handled carefully, will overshadow the throughput benefits. Unfortunately, until now, this aspect has been mostly ignored. To address this issue, we propose a method that considers the variable bit rate of the traffic and, depending on the current rate, minimizes the latency, while meeting the rate demand. We propose a persistent kernel based software architecture to overcome the challenges inherent in GPU implementation like kernel invocation overhead, CPU-GPU communication and memory access overhead. We have chosen packet classification as the packet processing application to demonstrate our technique. Using the proposed approach, we are able to reduce the packet latency on average by a factor of 3.5, compared to the state-of-the-art solutions, without any packet drop.

Large-scale integration with deep sub-micron technologies has led to high power densities and high chip working temperatures. At the same time, leakage energy has become the dominant energy consumption source of circuits due to reduced threshold voltages. Given the close interdependence between temperature and leakage current, temperature has become a major issue to be considered for power-aware system level design techniques. In this paper, we address the issue of leakage energy optimization through temperature aware idle time distribution (ITD). We first propose an offline ITD technique to optimize leakage energy consumption, where only static idle time is distributed. To account for the dynamic slack, we then propose an online ITD technique where both static and dynamic idle time are considered. To improve the efficiency of our ITD techniques, we also propose an analytical temperature analysis approach which is accurate and, yet, sufficiently fast to be used inside the energy optimization loop.

Supply voltage scaling and adaptive body biasing (ABB) 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 runtime, i.e., online. However, optimal voltage scaling algorithms are computationally expensive, and thus, if used online, significantly hamper the possible energy savings. To overcome the online complexity, we propose a quasi-static voltage scaling (QSVS) scheme, with a constant online time complexity O(1). This allows to increase the exploitable slack as well as to avoid the energy dissipated due to online recalculation of the voltage settings.

With new technologies, temperature has become a major issue to be considered at system level design. In this paper we propose a temperature aware idle time distribution technique for energy optimization with dynamic voltage scaling (DVS). A temperature analysis approach is also proposed which is accurate and, yet, sufficiently fast to be used inside the optimization loop for idle time distribution and voltage selection.

With new technologies, temperature has become a major issue to be considered at system level design. Without taking temperature aspects into consideration, no approach to energy or/and performance optimization will be sufficiently accurate and efficient. In this paper we propose an on-line temperature aware dynamic voltage and frequency scaling (DVFS) technique which is able to exploit both static and dynamic slack. The approach implies an offline temperature aware optimization step and on-line voltage/frequency settings based on temperature sensor readings. Most importantly, the presented approach is aware of the frequency/temperature dependency, by which important additional energy savings are obtained.

This paper proposes a novel approach to solve the allocation and scheduling problems for variable voltage/frequency multiprocessor systems-on-chip, which minimizes overall system energy dissipation. The optimality of derived system configurations is guaranteed, while the computation efficiency of the optimizer allows for solving problem instances that were traditionally considered beyond reach for exact solvers (optimality gap). Furthermore, this paper illustrates the development- and run-time software infrastructures that assist the user in developing applications and implementing optimizer solutions. The proposed approach guarantees a high level of power, performance, and constraint satisfaction predictability as from validation on the target platform, thus bridging the abstraction gap.

This paper proposes a temperature-aware dynamic voltage selection technique for energy minimization and presents a thorough analysis of the parameters that influence the potential gains that can be expected from such a technique, compared to a voltage selection approach that ignores temperature.

In multiprocessor systems, the traffic on the bus does not solely originate from data transfers due to data dependencies between tasks, but is also affected by memory transfers as result of cache misses. This has a huge impact on worst-case execution time (WCET) analysis and, in general, on the predictability of real-time applications implemented on such systems. As opposed to the WCET analysis performed for a single processor system, where the cache miss penalty is considered constant, in a multiprocessor system each cache miss has a variable penalty, depending on the bus contention. This affects the tasks' WCET which, however, is needed in order to perform system scheduling. At the same time, the WCET depends on the system schedule due to the bus interference. In this paper we present an approach to worst-case execution time analysis and system scheduling for real-time applications implemented on multiprocessor SoC architectures. The emphasis of this paper is on the bus scheduling policy and its optimization, which is of huge importance for the performance of such a predictable multiprocessor application.

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

FlexRay will very likely become the de-facto standard for in-vehicle communications. However, before it can be successfully used for safety-critical applications that require predictability, timing analysis techniques are necessary for providing bounds for the message communication times. In this paper, we propose techniques for determining the timing properties of messages transmitted in both the static (ST) and the dynamic (DYN) segments of a FlexRay communication cycle. The analysis techniques for messages are integrated in the context of a holistic schedulability analysis that computes the worst-case response times of all the tasks and messages in the system. We have evaluated the proposed analysis techniques using extensive experiments

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.

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.

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 system's 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.