Abstract not found

This paper is concerned with the derivation of infinite schedules for timed automata that are in some sense optimal. To cover a wide class of optimality criteria we start out by introducing an extension of the (priced) timed automata model that includes both costs and rewards as separate modelling features. A precise definition is then given of what constitutes optimal infinite behaviours for this class of models. We subsequently show that the derivation of optimal non-terminating schedules for such double-priced timed automata is computable.

Latency insensitive design has been recently proposed in literature as a way to design complex digital systems, whose functional behavior is robust with respect to arbitrary variations in interconnect latency. However, this approach does not guarantee the same robustness for the performance of the design, which indeed can experience big losses. This paper presents a simple, yet rigorous, method to (1) model the key properties of a latency insensitive system, (2) analyze the impact of interconnect latency on the overall throughput, and (3) optimize the performance of the final implementation.

The field of ubiquitous computing envisages an era when the average consumer owns hundreds or thousands of mobile and embedded computing devices. These devices will perform actions based on the context of their users, and therefore ubiquitous systems will gather, collate and distribute much more personal information about individuals than computers do today. Much of this personal information will be considered private, and therefore mechanisms which allow users to control the dissemination of these data are vital. Location information is a particularly useful form of context in ubiquitous computing, yet its unconditional distribution can be very invasive.

Stochastic timed marked graphs are graphical models of concurrent systems such as asynchronous circuits, embedded systems, queuing networks, manufacturing systems, and many automatic control systems. Unlike earlier works in which delays must be fixed or exponential, we allow the models to include arbitrary delay distributions as long as they have finite means. For such models, one important problem is to determine the average Time Separations of Events (TSE's). For example, an efficient means of finding TSE's in such models of asynchronous circuits facilitates both performance analysis as well as performance-driven synthesis. Towards this end, we present a novel technique to obtain upper and lower bounds on the average TSE for arbitrary pairs of system events. The bounds are formulated using a finite segment of the infinite unfolding of the marked graph and can be efficiently evaluated either using statistical sampling or, in some special cases, analytical methods. The resulting bounds...

Synchronous Data Flow Graphs (SDFGs) are a useful tool for modeling and analyzing embedded data flow applications, both in a single processor and a multiprocessing context or for application mapping on platforms. Throughput analysis of these SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, the size of which can be exponentially larger than the size of the original graph. In this paper, we present a method for throughput analysis of SDFGs, based on explicit statespace exploration and we show that the method, despite its worst-case complexity, works well in practice, while existing methods often fail. We demonstrate this by comparing the method with state-of-the-art cycle mean computation algorithms. Moreover, since the state-space exploration method is essentially the same as simulation of the graph, the results of this paper can be easily obtained as a byproduct in existing simulation tools. 1

We present a comprehensive experimental study of ten leading algorithms for the minimum mean cycle problem. For most of these algorithms, there has not been a clear understanding of their performance in practice although theoretical bounds have been proved for their running times. Only an experimental study can shed light on whether changes in an algorithm that make its running time theoretically more efficient are worth the overhead in terms of their payoff in practice. To this end, our experimental study provides a great deal of insight. In our evaluation, we programmed these algorithms uniformly and efficiently. We systematically compared them on a test suite composed of random graphs as well as benchmark circuits. Above all, our experimental results provide important insights into the individual performance as well as relative performance of these algorithms in practice. One of the most surprising results of this study is that Howard's algorithm, a well known algorithm to the stoch...

Markov decision processes (MDPs) are models of dynamic decision making under uncertainty. These models arise in diverse applications and have been developed extensively in fields such as operations research, control engineering, and the decision sciences in general. Recent research, especially in artificial intelligence, has highlighted the significance of studying the computational properties of MDP problems. We address

This paper proposes for latency insensitive systems a performance optimization technique called channel buffer queue sizing, which is performed after relay station insertion in the physical design stage. It can be shown that proper queue sizing can reduce or even completely avoid the performance loss due to imbalanced relay stations insertion in reconvergent paths. Moreover, the problem of queue sizing and placement of the additional buffers for maximum performance is formulated and studied to properly allocate available chip areas in the layout to communication channels. An algorithm based on mixed integer linear programming is proposed. Experimental results show that queue sizing is effective in improving the performance of latency insensitive systems even under tight area constraints. Moreover, the proposed algorithm is sufficiently efficient in obtaining the optimal solution for systems of practical sizes. 1.

Abstract not found