Inter-beat interval (IBI) measurement enables estimation of heart-tare variability (HRV) which, in turn, can provide early indication of potential cardiovascular diseases (CVDs). However, extracting IBIs from noisy signals is challenging since the morphology of the signal gets distorted in the presence of noise. Electrocardiogram (ECG) of a person in heavy motion is highly corrupted with noise, known as motion-artifact, and IBI extracted from it is inaccurate. As a part of remote health monitoring and wearable system development, denoising ECG signals and estimating IBIs correctly from them have become an emerging topic among signal-processing researchers. Apart from conventional methods, deep-learning techniques have been successfully used in signal denoising recently, and diagnosis process has become easier, leading to accuracy levels that were previously unachievable. We propose a deep-learning approach leveraging tiramisu autoencoder model to suppress motion-artifact noise and make the R-peaks of the ECG signal prominent even in the presence of high-intensity motion. After denoising, IBIs are estimated more accurately expediting diagnosis tasks. Results illustrate that our method enables IBI estimation from noisy ECG signals with SNR up to -30 dB with average root mean square error (RMSE) of 13 milliseconds for estimated IBIs. At this noise level, our error percentage remains below 8% and outperforms other state-of-the-art techniques.

Voltage/Frequency Islands (VFIs) are practically used in multicore systems. VFIs cluster cores share the same Voltage/Frequency (V/F) levels for the entire application runtime. Using VFIs, switching V/F levels has less overhead per-core while saving more energy in exchange for tolerable execution delay. This paper targets the well-known K-means algorithm for clustering cores, where each cluster contains cores with similar computational workloads across application phases. K-means produces sub-optimal clusters when cores workloads do not have the same variation across all phases. Furthermore, the workload variations require running a VFI with a different V/F level per-phase. This paper presents a fast heuristic that facilitates clustering by sorting computational workloads per-phase before applying K-means. The VFIs V/F levels are dynamically adjusted to meet energy budget constraints. This framework provides a guideline for users to choose a number of clusters that satisfies system configurations and energy efficiency preferences. Three parameters are evaluated to represent the computational workloads: utilization, Instruction-Per-Cycle (IPC), and execution time. For an application with highly varying workload, execution time achieves 52 and 65% percent more energy efficiency compared to utilization and IPC, respectively. Results show that the degree of workload variations for different applications impacts changing or fixing VFIs V/F levels.

Per-core Dynamic Voltage and Frequency (V/F) Scaling (DVFS) is a well-known methodology for achieving energy efficiency in multicore systems. Heuristic DVFS techniques provide fast, suboptimal V/F predictions while Dynamic Programming (DP) methods solve smaller sub-problems iteratively and use their outcomes to evaluate V/F levels globally, but at the cost of overhead delays. We propose an efficient DP framework using the Viterbi algorithm, which uses the Energy-Delay Product (EDP) as an objective function to predict the best V/F levels using applications' profiled information, to minimize energy consumption and execution time. Experimental results show that our framework outperforms heuristics using the EDP criteria and provides near-optimal solutions when maximizing energy saving is as, or more, important than minimizing execution time penalty. In fact, across several benchmarks, our proposed algorithm provides from a 12 to 75 percent improvement in EDP compared to heuristic methods. Furthermore, using a Pareto frontier to evaluate solutions of the algorithms under study, we demonstrate that our framework's energy-time solution is on average only 9 percent worse than the optimal solution. In addition, we show that our dynamic programming solution is 3 to 18 percent closer to a theoretical lower-bound when compared to the studied heuristic methods.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Traditional multicore designs, based on the Network-on-Chip (NoC) paradigm, suffer from high latency, significant power consumption and temperature hotspots as the system size scales up due to the inherent multi-hop nature of the communication fabric. NoCs have been shown to achieve increased performance by inserting long-range wired links following the principles of small-world graphs [1]. Design and optimization of multi- and many-core systems on chip (SoCs) that exploit small-world effects have already been demonstrated [1]. Small-world graphs are characterized by many short-distance links between neighboring nodes as well as a few relatively long-distance direct shortcuts. These networks can be further improved by replacing the long-distance shortcuts with single-hop, energy-efficient, wireless links. Wireless NoCs (WiNoCs) with millimeter (mm)-wave wireless links working at 10-100 GHz is envisioned as an enabling technology to design low-power and high-bandwidth massive multicore architectures [2]. These mm-wave small-world NoC (mSWNoC) networks are used as the interconnection backbone in this work.

The mm-wave small-world wireless NoC (mSWNoC) has emerged as an enabling interconnection infrastructure for designing high-bandwidth and energy-efficient multicore chips. In this mSWNoC architecture, long-range communication predominately takes place through the wireless shortcuts operating in the range of 10--100GHz, whereas short-range data exchange occurs through conventional metal wires. This results in performance advantages (lower latency and energy dissipation), mainly stemming from using the wireless links as long-range shortcuts between far-apart cores. The performance gain introduced by the wireless channels can be enhanced further if the wireline links of the mSWNoC are optimized according to the traffic patterns arising out of the application workloads. While there is significant energy savings, and hence temperature reduction, in the network due to the mSWNoC architecture, a load-imbalanced network is still susceptible to local temperature hotspots. In this work, we demonstrate that by incorporating congestion-avoidance routing with network-level dynamic voltage and frequency scaling (DVFS) in an mSWNoC, the power and thermal profiles can be improved without a significant impact on the overall network performance. In this work, we demonstrate how novel interconnect architectures enabled by the on-chip wireless links coupled with power management strategies can improve the energy and thermal characteristics of an mSWNoC significantly without introducing any performance degradation with respect to the conventional mesh-based NoC.

Wireless Network-on-Chip (WiNoC) has emerged as an enabling technology to design low power and high bandwidth massive multicore chips. WiNoCs based on small-world network architecture and designed with incorporating millimeter (mm)-wave on-chip wireless links offer significantly lower power and higher bandwidth compared to traditional mesh-based counterparts. In this mm-wave small-world WiNoC (mSWNoC), long distance communication predominately takes place through the wireless shortcuts whereas the short-range data exchange still occurs through the conventional metal wires. This results in performance advantages mainly stemming from using the wireless links as long-range shortcuts between far apart cores. This performance gain can be enhanced further if the wireline links and the processing cores of the WiNoC are optimized according to the traffic patterns and application workloads. In this work, we demonstrate that by incorporating both processor- and network-level dynamic voltage and frequency scaling (DVFS) in an mSWNoC, the power and thermal profiles can be improved without a significant impact on the overall execution time. We also show that depending on the applications, temperature hotspots can be formed either in the processing cores or in the network infrastructure. The proposed dual-level DVFS is capable of addressing both types of hotspots. In this work we will demonstrate how novel interconnect architectures enabled by the on-chip wireless links coupled with power management strategies can improve the energy and thermal characteristics of a NoC significantly.

Traditional multi-core designs, based on the Network-on-Chip (NoC) paradigm, suffer from high latency and power dissipation as the system size scales up due to the inherent multi-hop nature of communication. Introducing long-range, low power, and high-bandwidth, single-hop links between far apart cores can significantly enhance the performance of NoC fabrics. In this paper, we propose design of a small-world network based NoC architecture with on-chip millimeter (mm)-wave wireless links. The millimeter wave small-world NoC (mSWNoC) is capable of improving the overall latency and energy dissipation characteristics compared to the conventional mesh-based counterpart. The mSWNoC helps in improving the energy dissipation, and hence the thermal profile, even further in presence of network-level dynamic voltage and frequency scaling (DVFS) without incurring any additional latency penalty.

Current commercial systems on chip (SoC) designs integrate an increasingly large number of pre-designed cores and their number is predicted to increase significantly in the near future. Specifically, molecular-scale computing will allow single or even multiple order-of-magnitude improvements in device densities. In the design of high-performance massive multi-core chips, power and temperature have become dominant constraints. Increased power consumption can raise chip temperature,which in turn can decrease chip reliability and performance and increase cooling costs.The new, ensuing possibilities in terms of single chip integration call for new paradigms, architectures, and infrastructures for high bandwidth and low-power interconnects. In this paper we demonstrate how small-world Network-on-Chip (NoC) architectures with long-range wireless links enable design of energy and thermally efficient sustainable multi-core platforms.

This paper presents the design, deployment, and evaluation of a real-world sensor network system in an active volcano - Mount St. Helens. In volcano monitoring, the maintenance is extremely hard and system robustness is one of the biggest concerns. However, most system research to date has focused more on performance improvement and less on system robustness. In our system design, to address this challenge, automatic fault detection and recovery mechanisms were designed to autonomously roll the system back to the initial state if exceptions occur. To enable remote management, we designed a configurable sensing and flexible remote command and control mechanism with the support of a reliable dissemination protocol. To maximize data quality, we designed event detection algorithms to identify volcanic events and prioritize the data, and then deliver higher priority data with higher delivery ratio with an adaptive data transmission protocol. Also, a light-weight adaptive linear predictive compression algorithm and localized TDMA MAC protocol were designed to improve network throughput. With these techniques and other improvements on intelligence and robustness based on a previous trial deployment, we air-dropped 13 stations into the crater and around the flanks of Mount St. Helens in July 2009. During the deployment, the nodes autonomously discovered each other even in-the-sky and formed a smart mesh network for data delivery immediately. We conducted rigorous system evaluations and discovered many interesting findings on data quality, radio connectivity, network performance, as well as the influence of environmental factors.

No abstract available.

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than an alternative recent work of lossless compression, S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency. We also implement the algorithm in our real sensor network, and show that our linear prediction based compression algorithm significantly improves data reliability and network efficiency.

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five self-contained stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multihop wireless network. The transmit distance between stations was up to 8 km with favorable topography. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design of a robust sensor network optimized for rapid deployment during periods of volcanic unrest and provide real-time long-term volcano monitoring. The system supports UTC-time-synchronized data acquisition with 1 ms accuracy, and is remotely configurable. It has been tested in the lab environment, the outdoor campus, and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 160 km per hour, the sensor network has achieved a remarkable packet delivery ratio above 99 percent with an overall system uptime of about 93.8 percent over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system demonstrated to discipline scientists that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multi-hop wireless network. The distance between stations is up to 2 km. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design and evaluation of a robust sensor network to replace data loggers and provide real-time long-term volcano monitoring. The system supports UTCtime synchronized data acquisition with 1ms accuracy, and is online configurable. It has been tested in the lab environment, the outdoor campus and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 120 miles per hour, the sensor network has achieved a remarkable packet delivery ratio above 99 % with an overall system uptime of about 93.8 % over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system have alleviated the doubts of domain scientists and prove to them that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

We present a lightweight lossless compression algorithm for realtime sensor networks. Our proposed adaptive linear filtering compression (ALFC) algorithm performs predictive compression, using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets. Adaptive prediction eliminates the need to determine prediction coefficients a priori and, more importantly, allows compression to dynamically adjust to a changing source. The algorithm requires only integer arithmetic operations and thus is compatible with sensor platforms that do not support floating-point operations. Significant robustness to packets losses is provided by including small but sufficient overhead data to allow samples in each packet to be independently decoded. Real-world evaluations on seismic data from a wireless sensor network testbed show that ALFC provides more effective compression and uses less resources than some other lossless compression approaches such as S-LZW. Experiments in a multi-hop sensor network also show that ALFC can significantly improve raw data throughput and energy efficiency.

Earlier sensor network MAC protocols focus on energy conservation in low-duty cycle applications, while some recent applications involve real-time high-data-rate signals. This motivates us to design an innovative localized TDMA MAC protocol to achieve high throughput and low congestion in data collection sensor networks, besides energy conservation. TreeMAC divides a time cycle into frames and frame into slots. Parent determines children's frame assignment based on their relative bandwidth demand, and each node calculates its own slot assignment based on its hop-count to the sink. This innovative 2-dimensional frame-slot assignment algorithm has the following nice theory properties. Firstly, given any node, at any time slot, there is at most one active sender in its neighborhood (including itself). Secondly, the packet scheduling with TreeMAC is bufferless, which therefore minimizes the probability of network congestion. Thirdly, the data throughput to gateway is at least 1/3 of the optimum assuming reliable links. Our experiments on a 24 node test bed demonstrate that TreeMAC protocol signi ficantly improves network throughput and energy efficiency, by comparing to the TinyOS's default CSMA MAC protocol and a recent TDMA MAC protocol Funneling-MAC.

This paper present a wake-on sensor network formed with the wake-on motes, TelosW. Our wake-on hardware and software design enable lower power operations and longer network lifetime.

In this paper, we propose a lightweight and transparent management framework for TinyOS sensor networks, called L-SNMS, which minimizes the overhead of management functions, including memory usage overhead, network traffic overhead, and integration overhead. We accomplish this by making L-SNMS virtually transparent to other applications hence requiring minimal integration. The proposed L-SNMS framework has been successfully tested on various sensor node platforms, including TelosB, MICAz and IMote2.

The paper proposes a novel improvised architecture for providing fundamental services through exploitation of node heterogeneity so that relatively stable and capable devices cooperate and assist resource-poor devices. The novel hierarchical architecture called NeVo (Network of Volunteers) uses a small subset of the volunteer nodes to perform basic services such as QoS provisioning, service discovery, mobility management, and trust management in pervasive environments. Service discovery protocols and trust management schemes that utilize the NeVo architecture are presented. The utility of NeVo as a flexible and adaptable architecture for creating and provisioning fundamental services is validated through extensive simulation studies.

Service-oriented architectures (SOAs) promise to provide transparency to resource access by exposing the resources available as services. SOAs have been employed within pervasive computing systems to provide essential support to user tasks by creating services representing the available resources. The mechanism of combining two or more basic services into a possibly complex service is known as service composition. Existing solutions to service composition employ a template-matching approach, where the user needs are expressed as a request template, and through composition, a system would identify services to populate the entities within the request template. However, with the dynamism involved in pervasive environments, the user needs have to be met by exploiting available resources, even when an exact match does not exist. In this paper, we present a novel service composition mechanism for pervasive computing. We employ the service-oriented middleware platform called Pervasive Information Communities Organization (PICO) to model and represent resources as services. The proposed service composition mechanism models services as directed attributed graphs, maintains a repository of service graphs, and dynamically combines multiple basic services into complex services. Further, we present a hierarchical overlay structure created among the devices to exploit the resource unevenness, resulting in the capability of providing essential service-related support to resource-poor devices. Results of extensive simulation studies are presented to illustrate the suitability of the proposed mechanism in meeting the challenges of pervasive computing-user mobility, heterogeneity, and the uncertain nature of involved resources.

Increasing popularity of the pervasive computing paradigm on one hand and the technological developments on the other, have paved the way for development and deployment of pervasive services in the everyday habitat. The service providers, who build, operate and manage services would want to maximize their revenues, and at the same time, the users would seek guarantees to support their applications. While there are a number of mechanisms proposed to build and operate service oriented architectures (SOAs) in pervasive computing arena, very little work has been done in terms of evaluating such schemes. The work presented in this paper proposes mechanisms to evaluate SOAs in pervasive computing. We also present a mechanism to evaluate service composition techniques, which are an effective way of delivering services to the end user. In earlier work, we proposed a service composition mechanism in pervasive computing environments called seamless service composition (SeSCo). We evaluate and compare SeSCo with a discover+match mechanism for the deployment of pervasive services within an automobile. We also present performance results of the two approaches through simulation.

In this paper we present an adaptive application-driven power management (AADPM) strategy with online idle period length distribution learning capability for the IEEE 802.11b WLAN. We discuss its design and evaluate the performance in comparison with other power management strategies using the network simulator NS2. We simulated both the single user and multiple users scenarios. Experimental results have shown that, compared with other power management methods examined in this paper, AADPM achieved the highest energy saving in all cases and demonstrated strong adaptability to network congestion.

Digital multimedia has gained popularity due to the huge success of the Internet. Users acquire and disseminate multimedia information using different types of devices and communication channels. On the other hand, personal devices, such as laptops, PDAs and smart phones, are not only capable of producing and rendering multimedia, but also support associated computing and communication tasks. However, there is a lack of support for ensuring automated, continuous access to multimedia in the presence of dynamicity and heterogeneity. Pervasive computing concepts can be effectively employed to provide anytime anywhere multimedia support to users over heterogeneous wireless environments. We present a novel approach to support adaptive services for multimedia delivery in heterogeneous wireless networks. The service adaptation and composition mechanism exploits middleware tools developed for pervasive information communities of software agents. We also demonstrate the feasibility of the proposed scheme through an intuitive scenario, and present our prototype results.

A large fraction of Internet users access network resources through Web clients/browsers. The quality of service (QoS) experienced by their Internet users has gained great importance in recent years. Furthermore, new Web applications require the delivery of multimedia data in real time (e.g., streaming stored video and audio) and information transfer through the Internet is becoming one of the principal paradigms for business: electronic sales, banking, finance, collaborative work are a few examples of this. The QoS perceived by its users is thus becoming a dominant factor for the success of an Internet-based Web service. The principal QoS attributes users perceive include those related to the service "responsiveness", i.e., the service availability and timeliness.

This paper presents a characterization of various current approaches to quality of service (QoS) management in distributed, real-time systems. The characterization is based on an eight-point comparison of selected research: middleware interface, handling of violations, support of distributed applications, monitored data, requirements specification, characteristics of the supported real-time systems, overall system architecture, and quality of service dimensions supported. The comparisons are summarized in a tabular form with key references for further reading.

Time constrained systems which operate in dynamic environments may have unknown worst case scenarios, may have large variances in the sizes of the data and event sets that they process (and thus, have large variances in execution latencies and resource requirements), and may not be statically characterizable, even by time invariant statistical distributions. The paper presents a specification language for describing environment dependent features. Also presented is an abstract model that is constructed (statically) from the specifications, and is augmented (dynamically,) with the state of environment dependent features. The model is used to define techniques for QoS (quality of service) monitoring, QoS diagnosis, and resource allocation analysis. Experimental results show the effectiveness of the approach for specification of real time QoS, detection and diagnosis of QoS failures, and restoration of acceptable QoS via reallocation of distributed computer and network resources.

The task scheduling policy and the processor allocation scheme affect the system performance significantly. In this paper, we propose an efficient processor allocation scheme for 3D mesh interconnection network with a simple FIFO scheduling policy. Complexity analysis shows that the allocation and deallocation of the scheme are O(LWH/sup 2/) and O(LH), respectively, which are better than earlier schemes. Comprehensive computer simulation shows that the average allocation time of the proposed scheme is improved up to about 85% compared to the best earlier 3D approach.

Scheduling parallel tasks represented as a Directed Acyclic Graph (DAG), on a multiprocessor system has been an important research area in the past decades. One of the critical aspects of a class of scheduling algorithms, called "List Scheduling", is how to decide which task is to be scheduled next. This is achieved by assigning priorities to the nodes or the edges of the input DAG, and thus the task with the highest priority will be scheduled next. This paper proposes a low complexity scheduling algorithm to improve the priority node selection criteria in list scheduling algorithms. The worst case performance of the proposed algorithm is analyzed for general input DAGs. Also, the worst case performance and the optimality conditions are obtained for free structured input DAGs. The performance comparison study shows that the proposed algorithm outperforms existing scheduling algorithms especially for input DAGs with high communication overheads. The performance improvement over existing algorithms becomes larger as the input DAG becomes more dense and the level of parallelism in the DAG is increased.

Efficient allocation of processors to incoming tasks in parallel computer systems is very important for achieving the desired high performance. It requires recognizing the free available processors with minimum overhead. In this paper, we present an efficient task allocation scheme for 2D mesh architectures. By employing a new approach for searching the mesh, our scheme can find the available submesh without scanning the entire mesh, unlike earlier designs. Comprehensive computer simulation reveals that the average allocation time and waiting delay are much smaller than earlier schemes of comparable performances, irrespective of the size of meshes and distribution of the shape of the incoming tasks.

Abstract One of the major issues that needs to be addressed in distributed memory multiprocessor (DMM) systems is the program task partitioning and scheduling problems, i.e. mapping of an application program's precedence related task threads among the processing elements of a DMM system. The optimal task partitioning and scheduling problem, with the goal of minimizing the program execution time and interprocessor communication overhead, is known to be an NP‐complete problem. The paper addresses the design, development and performance evaluation of a novel static task partitioning and scheduling method called linear clustering with task duplication (LCTD). LCTD employs the linear (sequential) execution of tasks and task duplication heuristics in achieving minimized computation and interprocessor communication delays in DMMs. The superiority of the proposed LCTD algorithm is demonstrated through simulation studies and comparison against several of the existing static scheduling schemes, such as heavy node first (HNF) and linear clustering. We show that the proposed method can obtain an average of 33% improvement in program execution time and 21% improvement in processor utilization compared to linear clustering and HNF methods.

Efficient partitioning and scheduling of parallel programs and the distribution of data among pmessing elements are very important issues in parallel and distributed systems. Existing tools fdl short in addressing the issues satisfactorily. On one hand, it is believed to k unreasonable to leave the burden of these complex tasks to the programmers. On the other hand, fully automated schedulers have ken shown to be d little practical significance, or suitable only for restricted cases. In this paper we address the issues and algorithms for efficient partitioning and scheduling d parallel programs, including the distribution of data, in dislributed-memory multiprocessor systems, using the PARSA parallel software development environment. PARSA consists of a set of visual, interactive, compiletime tools that will provide automated program partitions and schedules whenever possible, while permitting the user to exert conml over these operations for a htter performance. The support program assessment tool provides the users the opportunity b fine-tune the program and achieve their performance objectives.

A multi-language-based data interface system for heterogeneous distributed processing is introduced. A prototyped environment based on this system is discussed, and an evaluation of the prototyped system is presented. It is shown that by keeping the syntax of the specification language flexible and close to existing high-level languages, a user can learn the interface language quickly. Semantically, this data interface views structured data as consisting of two parts: the data values themselves and the representation of the structure among the data values. Through this separation, it is possible to have pipelined data type checking and data conversion operations.>

The asynchronous nature of the dataflow model of computation allows the exploitation of maximum inherent parallelism in many application programs. However, before the dataflow model of computation can become a viable alternative to the control flow model of computation, one has to find practical solutions to some problems such as efficient handling of data structures. The paper introduces a new model for handling data structures in a dataflow environment. The proposed model combines constant time access capabilities of vectors as well as the flexibility inherent in the concept of pointers. This allows a careful balance between copying and sharing to optimize the storage and processing overhead incurred during the operations on data structures. The mode) is compared by simulation to other data structure models proposed in the literature, and the results are good.>

The notion of CPRS (concurrent processing which result sharing) graph decomposition is presented which is based on problem dynamics, and the validity of the CPRS scheme in a dataflow environment is explored. A dynamic dataflow architecture to execute programs under the new model of computation, called the CPRS architecture, is presented. An analytical modeling, based on a closed queueing network model, is used to study the effect of result sharing on the performance of the CPRS architecture.>

The authors present a rigorous matrix-based analysis of the Hopfield model in order to understand the fundamental characteristics of the model, its strengths, and its weaknesses. They formally show why the results obtained by applying the Hopfield model to most NP-complete problems are in general unsatisfactory. They also indicate that promising results are achieved randomly. Possible amendments and future research directions are discussed.>

article Free Access Share on A systolic multiplier unit and its VLSI design Authors: A. R. Hurson School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, Oklahoma School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, OklahomaView Profile , B. Shirazi School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, Oklahoma School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, OklahomaView Profile Authors Info & Claims ACM SIGARCH Computer Architecture NewsVolume 13Issue 3June 1985 pp 302–309https://doi.org/10.1145/327070.327274Published:01 June 1985Publication History 1citation428DownloadsMetricsTotal Citations1Total Downloads428Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF