Vincenzo Catania

Bio: Vincenzo Catania is an academic researcher from University of Catania. The author has contributed to research in topics: Fuzzy logic & Network on a chip. The author has an hindex of 27, co-authored 203 publications receiving 3170 citations.

Noxim: An open, extensible and cycle-accurate network on chip simulator

Abstract: Emerging on-chip communication technologies like wireless Networks-on-Chip (WiNoCs) have been proposed as candidate solutions for addressing the scalability limitations of conventional multi-hop NoC architectures. In a WiNoC, a subset of network nodes are equipped with a wireless interface which allows them long-range communication in a single hop. This paper presents Noxim, an open, configurable, extendible, cycle-accurate NoC simulator developed in SystemC which allows to analyze the performance and power figures of both conventional wired NoC and emerging WiNoC architectures.

Implementation and Analysis of a New Selection Strategy for Adaptive Routing in Networks-on-Chip

Abstract: Efficient and deadlock-free routing is critical to the performance of networks-on-chip. The effectiveness of any adaptive routing algorithm strongly depends on the underlying selection strategy. A selection function is used to select the output channel where the packet will be forwarded on. In this paper we present a novel selection strategy that can be coupled with any adaptive routing algorithm. The proposed selection strategy is based on the concept of Neighbors-on-Path the aims of which is to exploit the situations of indecision occurring when the routing function returns several admissible output channels. The overall objective is to choose the channel that will allow the packet to be routed to its destination along a path that is as free as possible of congested nodes. Performance evaluation is carried out by using a flit-accurate simulator under traffic scenarios generated by both synthetic and real applications. Results obtained show how the proposed selection strategy applied to the Odd-Even routing algorithm yields an improvement in both average delay and saturation point up to 20% and 30% on average respectively, with a minimal overhead in terms of area occupation. In addition, a positive effect on total energy consumption is also observed under near-congestion packet injection rates.

Multi-objective mapping for mesh-based NoC architectures

Abstract: In this paper we present an approach to multi-objective exploration of the mapping space of a mesh-based network-on-chip architecture. Based on evolutionary computing techniques, the approach is an efficient and accurate way to obtain the Pareto mappings that optimize performance and power consumption. Integration of the approach in an exploration framework with a kernel based on an event-driven trace-based simulator makes it possible to take account of important dynamic effects that have a great impact on mapping. Validation on both synthesized traffic and real applications (an MPEG-2 encoder/decoder system) confirms the efficiency, accuracy and scalability of the approach.

Cycle-Accurate Network on Chip Simulation with Noxim

Abstract: The on-chip communication in current Chip-MultiProcessors (CMP) and MultiProcessor-SoC (MPSoC) is mainly based on the Network-on-Chip (NoC) design paradigm. Unfortunately, it is foreseen that conventional NoC architectures cannot sustain the performance, power, and reliability requirements demanded by the next generation of manycore architectures. Recently, emerging on-chip communication technologies, like wireless Networks-on-Chip (WiNoCs), have been proposed as candidate solutions for addressing the scalability limitations of conventional multi-hop NoC architectures. In a WiNoC, a subset of network nodes are equipped with a wireless interface which allows them long-range communication in a single hop. Assessing the performance and power figures of NoC and WiNoC architectures requires the availability of simulation tools that are often limited on modeling specific network configurations. This article presents Noxim, an open, configurable, extendible, cycle-accurate NoC simulator developed in SystemC, which allows to analyze the performance and power figures of both conventional wired NoC and emerging WiNoC architectures.

Application Specific Routing Algorithms for Networks on Chip

Abstract: In this paper we present a methodology to develop efficient and deadlock free routing algorithms for Network-on-Chip (NoC) platforms which are specialized for an application or a set of concurrent applications. The proposed methodology, called application specific routing algorithm (APSRA), exploits the application specific information regarding pairs of cores which communicate and other pairs which never communicate in the NoC platform to maximize communication adaptivity and performance. The methodology also exploits the known information regarding concurrency/non-concurrency of communication transactions among cores for the same purpose. We demonstrate, through analysis of adaptivity as well as simulation based evaluation of latency and throughput, that algorithms produced by the proposed methodology give significantly higher performance as compared to other deadlock free algorithms for both homogeneous as well as heterogeneous 2D mesh topology NoC systems. For example, for homogeneous mesh NoC, APSRA results in approximately 30% less average delay as compared to odd-even algorithm just below saturation load. Similarly the saturation load point for APSRA is significantly higher as compared to other adaptive routing algorithms for both homogeneous and non-homogeneous mesh networks.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Multiobjective evolutionary algorithms: A survey of the state of the art

Abstract: A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.