George I. Stamoulis

Fast transform-based preconditioners for large-scale power grid analysis on massively parallel architectures

Efficient analysis of massive on-chip power delivery networks is among the most challenging problems facing the EDA industry today. In this paper, we present a new preconditioned iterative method for fast DC and transient simulation of large-scale power grids found in contemporary nanometer-scale ICs. The emphasis is placed on the preconditioner which reduces the number of iterations by a factor of 5X for a 2.6M-node industrial design and by 72.6X for a 6.2M-node synthetic benchmark, compared with incomplete factorization preconditioners. Moreover, owing to the preconditioners special structure that allows utilizing a Fast Transform solver, the preconditioning system can be solved in a near-optimal number of operations, while it is extremely amenable to parallel computation on massively parallel architectures like graphics processing units (GPUs). Experimental results demonstrate that our method achieves a speed-up of 214.3X and 138.7X for a 2.6M-node industrial design, and a speed-up of 1610.5X and 438X for a 3.1M-node synthetic design, over state-of-the-art direct and iterative solvers respectively when GPUs are utilized. At the same time, its matrix-less formulation allows for reducing the memory footprint by up to 33% compared to the memory requirements of the best available iterative solver.

Power reduction in an H.264 encoder through algorithmic and logic transformations

The H.264 video coding standard can achieve considerably higher coding efficiency than previous video coding standards. The keys to this high coding efficiency are the two prediction modes (intra & inter) provided by H.264. Unfortunately, these result in a considerably higher encoder complexity that adversely affects speed and power, which are both significant for the mobile multimedia applications targeted by the standard. Therefore, it is of high importance to design architectures that minimize the speed and power overhead of the prediction modes. In this paper we present a new algorithm, and the logic transformations that enable it, that can replace the standard sum of absolute differences (SAD) approach in the two main prediction modes, and provide a power efficient hardware implementation without perceivable degradation in coding efficiency or video quality

A 3-D Fast Transform-based preconditioner for large-scale power grid analysis on massively parallel architectures

Efficient analysis of on-chip power delivery networks is one of the most challenging problems that EDA is confronted with. This paper addresses the problem of simulating general multi-layer power delivery networks with significant via resistances. An iterative solution method is combined with an efficient and extremely parallel preconditioning mechanism based on the application of a 3D Fast Transform solver, which enables harnessing the computational resources of massively parallel architectures, such as GPUs. Experimental evaluation of the proposed methodology on a set of large-scale industrial benchmarks demonstrates a speed-up of 290.2X for a 2.62M-node design over a state-of-the-art parallel direct solver, and a speed-up of 75.5X for a 10.51M-node design over a parallel iterative solver with a general-purpose preconditioner, when GPUs are utilized.

Characterization of the worst-case current waveform excitations in general RLC-model power grid analysis

Validating the robustness of power distribution in modern IC design is a crucial but very difficult problem, due to the vast number of possible working modes and the high operating frequencies which necessitate the modeling of power grid as a general RLC network. In this paper we provide a characterization of the worst-case current waveform excitations that produce the maximum voltage drop among all possible working modes of the IC. In addition, we give a practical methodology to estimate these worst-case excitations on the basis of a sample of the excitation space acquired via plain circuit simulation. In the course of characterizing the worst-case excitations we also establish that the voltage drop function for RLC grid models has nonnegative coefficients, which has been an open problem so far.

Supporting Service Differentiation in Wireless Sensor Networks

The evolution of wireless sensor technology allows for the provision of enhanced services to miscellaneous application domains. In parallel, Quality of Service (QoS) support becomes necessary to satisfy the needs of these new service models. This paper presents QoS requirements from a service model perspective and describes challenges for QoS support in WSNs. We also provide a review of current efforts in Medium Access Control (MAC) QoS support in WSNs. Then, we investigate various performance metrics of IEEE 802.15.4 standard in order to determine the technological issues that arise. From the outcome of the experiments conducted, using ns-2, we identified that different schemes of services and application scenarios for different ways of deployment, scales of network and traffic load can satisfy diverse user needs and requirements.

The Implementation of a Web Application for Screening Children with Dyslexia

Developmental dyslexia is defined as an unexpected specific and persistent failure to acquire efficient reading skills despite conventional instruction, adequate intelligence and sociocultural opportunity. The main aim of the present research protocol was to construct a web screening battery of tests in order to screen children’s reading, orthographic and cognitive skills. The main hypothesis of the present study was that Greek students that were already diagnosed as dyslexic, shall present significantly lower scores. A total of sixty, right handed children (30 male and 30 female, age range 8–11 years old M = 8.93, SD = 0.83) participated in this study. Reading, cognitive and orthographic skills of children with dyslexia and typically achieving children were examined, using a battery that consisted of seven tasks. Subsequent analysis of variance revealed that the children with dyslexia had statistically significant (p < 0.01) lower mean scores of correct answers and larger latencies in all tasks compared to their average peers that participated at the control group.

Cross-layer energy-aware topology control through Simulated Annealing for WSNs

In this paper, we optimize the energy consumption and node placements of a wireless sensor network (WSN) using Simulated Annealing (SA). We propose an energy-aware optimization of the power control, the packet transmission and the topology control in WSNs. An optimization problem of minimizing the energy consumption, which incorporates the power control, the packet transmission through error correcting codes (ECC) and the topology control fulfilling specific coverage constraints, is formulated. The considered joint optimization problem is proved NP-complete and for that reason a heuristic method based on SA is applied. By applying SA algorithm, an updated topology is evaluated at any convergence point and thus a near-optimal transmit power and packet length, in conjunction with node placements is achieved jointly. Our results confirm that the proposed near optimal topology deployment proposed by SA leads to greater network coverage and lower energy consumption as compared to a Random Sampling (RS) heuristic.

Selective Inversion of Inductance Matrix for Large-Scale Sparse RLC Simulation

The inverse of the inductance matrix (reluctance matrix) is amenable to sparsification to a much greater extent than the inductance matrix itself. However, the inversion and subsequent truncation of a large dense inductance matrix to obtain the sparse inverse is very time-consuming, and previously proposed window-based techniques cannot provide adequate accuracy. In this paper we propose a method for selective inversion of the inductance matrix to a prescribed sparsity ratio, which is also amenable to parallelization on modern architectures. Experimental results demonstrate its potential to provide efficient and accurate approximation of the reluctance matrix for simulation of large-scale RLC circuits.

An Automatic Multi-Agent Web Image and Associated Keywords Retrieval System

Web-based image search engines and CBIR techniques are blind to the actual content. As a result querying for a specific object is often cluttered with irrelevant data, leading to low precision. Furthermore recall rates are also very low since retrieval procedures are usually based either on context (surrounding text) and file captions or on low-level visual features. In this paper an automatic multi-agent image retrieval system is proposed. Our novel system exploits the format of multimedia sharing Web sites to discover the underlying structure in order to finally infer and extract multimedia files and corresponding associated keywords from the Web pages. The system first identifies the section of the Web page that contains the multimedia file to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. Experimental results on real-world image sharing Web sites are presented and discussed in this paper, indicating the promising performance of the proposed system.

Differences in dyslexic students before and after a remediation program: A clinical neuropsychological and event related potential study

ABSTRACT Developmental dyslexia is defined as an unexpected specific and persistent failure to acquire efficient reading skills despite conventional instruction, adequate intelligence and sociocultural opportunity. This article reports the outcomes of a study that evaluated the implementation of a 4-month intervention program. The intervention consisted of structured activities aiming at improving (a) the children’s phonological awareness, (b) their visual and auditory memory, (c) their visual discrimination ability, and (d) their text comprehension. Participants were 12 children diagnosed as dyslexic matched with 12 typically achieving peers of similar age and gender. Baseline assessment consisted of a clinical neuropsychological battery of tests and Event Related Potentials (ERPs) and resulted in confirming the discrepancy between the dyslexic and the control group. Following the remediation program, the dyslexic group did not differ significantly from their control group in six out of eight neuropsychological tests. The electrophysiological results revealed that the two groups had similar P300 latencies in 12 out of the 15 electroencephalographic sites assessed. These findings suggest that children with dyslexia can improve their abilities through a remediation program which aims to strengthen their audio-visual and phonological processes along with their working memory capability.