Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

MOCAST 2021

The 10th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2021) will take place in Thessaloniki, Greece, from July 5th to July 7th, 2021. The MOCAST technical program includes all aspects of circuit and system technologies, from modeling to design, verification, implementation, and application. This Special Issue presents extended versions of top-ranking papers in the conference. The topics of MOCAST include:Analog/RF and mixed signal circuits;Digital circuits and systems design;Nonlinear circuits and systems;Device and circuit modeling;High-performance embedded systems;Systems and applications;Sensors and systems;Machine learning and AI applications;Communication; Network systems;Power management;Imagers, MEMS, medical, and displays;Radiation front ends (nuclear and space application);Education in circuits, systems, and communications

Improving the profitability, availability and condition monitoring of FPSO terminals

The main focus of this study is to improve the profitability, availability and condition monitoring of Liquefied Natural Gas (LNG) Floating Production Storage and Offloading platforms (FPSOs). Propane pre-cooled, mixed refrigerant (C3MR) liquefaction is the key process in the production of LNG on FPSOs. LNG liquefaction system equipment has the highest failure rates among the other FPSO equipment, and thus the highest maintenance cost. Improvements in the profitability, availability and condition monitoring were made in two ways: firstly, by making recommendations for the use of redundancy in order to improve system reliability (and hence availability); and secondly, by developing an effective condition-monitoring algorithm that can be used as part of a condition-based maintenance system. C3MR liquefaction system reliability modelling was undertaken using the time-dependent Markov approach. Four different system options were studied, with varying degrees of redundancy. The results of the reliability analysis indicated that the introduction of a standby liquefaction system could be the best option for liquefaction plants in terms of reliability, availability and profitability; this is because the annual profits of medium-sized FPSOs (3MTPA) were estimated to increase by approximately US$296 million, rising from about US$1,190 million to US$1,485.98 million, if redundancy were implemented. The cost-benefit analysis results were based on the average LNG prices (US$500/ton) in 2013 and 2014. Typically, centrifugal turbines, compressors and blowers are the main items of equipment in LNG liquefaction plants. Because centrifugal equipment tops the FPSO equipment failure list, a Condition Monitoring (CM) system for such equipment was proposed and tested to reduce maintenance and shutdown costs, and also to reduce flaring. The proposed CM system was based on a novel FFT-based segmentation, feature selection and fault identification algorithm. A 20 HP industrial air compressor system with a rotational speed of 15,650 RPM was utilised to experimentally emulate five different typical centrifugal equipment machine conditions in the laboratory; this involved training and testing the proposed algorithm with a total of 105 datasets. The fault diagnosis performance of the algorithm was compared with other methods, namely standard FFT classifiers and Neural Network. A sensitivity analysis was performed in order to determine the effect of the time length and position of the signals on the diagnostic performance of the proposed fault identification algorithm. The algorithm was also checked for its ability to identify machine degradation using datasets for which the algorithm was not trained. Moreover, a characterisation table that prioritises the different fault detection techniques and signal features for the diagnosis of centrifugal equipment faults, was introduced to determine the best fault identification technique and signal feature. The results suggested that the proposed automated feature selection and fault identification algorithm is effective and competitive as it yielded a fault identification performance of 100% in 3.5 seconds only in comparison to 57.2 seconds for NN. The sensitivity analysis showed that the algorithm is robust as its fault identification performance was affected by neither the time length nor the position of signals. The characterisation study demonstrated the effectiveness of the AE spectral feature technique over the fault identification techniques and signal features tested in the course of diagnosing centrifugal equipment faults. Moreover, the algorithm performed well in the identification of machine degradation. In summary, the results of this study indicate that the proposed two-pronged approach has the potential to yield a highly reliable LNG liquefaction system with significantly improved availability and profitability profiles

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.Comment: Preprin

Programmable stochastic processors

As traditional approaches for reducing power in microprocessors are being exhausted, extreme power challenges call for unconventional approaches to power reduction. Recent research has shown substantial promise for application-specific stochastic computing, i.e., computing that exploits application error tolerance to enable careful relaxation of correctness guarantees provided by hardware in order to reduce power. This dissertation explores the feasibility, challenges, and potential benefits of stochastic computing in the context of programmable general purpose processors. Specifically, the dissertation describes design-level techniques that minimize the power of a processor for a non-zero error rate or allow a processor to fail gracefully when operated over a range of non-zero error rates. It presents microarchitectural design principles that allow a processor to trade off reliability and energy more efficiently to minimize energy when exploiting error resilience. It demonstrates the benefit of using compiler optimizations that optimize a binary to enable more energy savings when operating at a non-zero error rate. It also demonstrates significant benefits for a programmable stochastic processor prototype that improves energy efficiency by carefully relaxing correctness and exposing errors in applications running on a commodity processor. This dissertation on programmable stochastic processors conclusively shows that the architecture and design of processors and applications should be approached differently in scenarios where errors are allowed to be exposed from the hardware to higher levels of the compute stack. Significant energy benefits are demonstrated for design-, architecture-, compiler-, and application-level optimizations for general purpose programmable stochastic processors

Proceedings of the Third International Mobile Satellite Conference (IMSC 1993)

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial cellular communications services. While the first and second International Mobile Satellite Conferences (IMSC) mostly concentrated on technical advances, this Third IMSC also focuses on the increasing worldwide commercial activities in Mobile Satellite Services. Because of the large service areas provided by such systems, it is important to consider political and regulatory issues in addition to technical and user requirements issues. Topics covered include: the direct broadcast of audio programming from satellites; spacecraft technology; regulatory and policy considerations; advanced system concepts and analysis; propagation; and user requirements and applications

Design of robust ultra-low power platform for in-silicon machine learning

The rapid development of machine learning plays a key role in enabling next generation computing systems with enhanced intelligence. Present day machine learning systems adopt an "intelligence in the cloud" paradigm, resulting in heavy energy cost despite state-of-the-art performance. It is therefore of great interest to design embedded ultra-low power (ULP) platforms with in-silicon machine learning capability. A self-contained ULP platform consists of the energy delivery, sensing and information processing subsystems. This dissertation proposes techniques to design and optimize the ULP platform for in-silicon machine learning by exploring a trade-off that exists between energy-efficiency and robustness. This trade-off arises when the information processing functionality is integrated into the energy delivery, sensing, or emerging stochastic fabrics (e.g., CMOS operating in near-threshold voltage or voltage overscaling, and beyond CMOS devices). This dissertation presents the Compute VRM (C-VRM) to embed the information processing into the energy delivery subsystem. The C-VRM employs multiple voltage domain stacking and core swapping to achieve high total system energy efficiency in near/sub-threshold region. A prototype IC of the C-VRM is implemented in a 1.2 V, 130 nm CMOS process. Measured results indicate that the C-VRM has up to 44.8% savings in system-level energy per operation compared to the conventional system, and an efficiency ranging from 79% to 83% over an output voltage range of 0.52 V to 0.6 V. This dissertation further proposes the Compute Sensor approach to embed information processing into the sensing subsystem. The Compute Sensor eliminates both the traditional sensor-processor interface, and the high-SNR/high-energy digital processing by moving feature extraction and classification functions into the analog domain. Simulation results in 65 nm CMOS show that the proposed Compute Sensor can achieve a detection accuracy greater than 94.7% using the Caltech101 dataset, which is within 0.5% of that achieved by an ideal digital implementation. The performance is achieved with 7x to 17x lower energy than the conventional architecture for the same level of accuracy. To further explore the energy-efficiency vs. robustness trade-off, this dissertation explores the use of highly energy efficient but unreliable stochastic fabrics to implement in-silicon machine learning kernels. In order to perform reliable computation on the stochastic fabrics, this dissertation proposes to employ statistical error compensation (SEC) as an effective error compensation technique. This dissertation makes a contribution to the portfolio of SEC by proposing embedded algorithmic noise tolerance (E-ANT) for low overhead error compensation. E-ANT operates by reusing part of the main block as estimator and thus embedding the estimator into the main block. System level simulation results in a commercial 45 nm CMOS process show that E-ANT achieves up to 38% error tolerance and up to 51% energy savings compared with an uncompensated system. This dissertation makes a contribution to the theoretical understanding of stochastic fabrics by proposing a class of probabilistic error models that can accurately model the hardware errors on the stochastic fabrics. The models are validated in a commercial 45 nm CMOS process and employed to evaluate the performance of machine learning kernels in the presence of hardware errors. Performance prediction of a support vector machine (SVM) based classifier using these models indicates that the probability of detection P_{det} estimated using the proposed model is within 3% for timing errors due to voltage overscaling when the error rate p_η ≤ 80%, within 5% for timing errors due to process variation in near threshold-voltage (NTV) region (0.3 V-0.7 V) and within 2% for defect errors when the defect rate p_{saf} is between 10^{-3} and 20%, compared with HDL simulation results. Employing the proposed error model and evaluation methodology, this dissertation explores the use of distributed machine learning architectures, named classifier ensemble, to enhance the robustness of in-silicon machine learning kernels. Comparative study of distributed architectures (i.e., random forest (RF)) and centralized architectures (i.e., SVM) is performed in a commercial 45 nm CMOS process. Employing the UCI machine learning repository as input, it is determined that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors in the NTV region (0.3 V- 0.7 V). Additionally, an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric is proposed to further enhance the robustness of RF architectures. Simulation results confirm that the error weighted voting technique achieves a P_{det} that varies by only 1.4%, which is 12x lower compared to centralized architectures

Performance evaluation and model checking of probabilistic real-time actors

This dissertation is composed of two parts. In the first part, performance evaluation and verification of safety properties are provided for real-time actors. Recently, the actor-based language, Timed Rebeca, was introduced to model distributed and asynchronous systems with timing constraints and message passing communication. A toolset was developed for automated translation of Timed Rebeca models to Erlang. The translated code can be executed using a timed extension of McErlang for model checking and simulation. In the first part of this dissertation, we induce a new toolset that provides statistical model checking of Timed Rebeca models. Using statistical model checking, we are now able to verify larger models against safety properties comparing to McErlang model checking. We examine the typical case studies of elevators and ticket service to show the efficiency of statistical model checking and applicability of our toolset. In the second part of this dissertation, we enhance our modeling ability and cover more properties by performance evaluation and model checking of probabilistic real-time actors. Distributed systems exhibit probabilistic and nondeterministic behaviors and may have time constraints. Probabilistic Timed Rebeca (PTRebeca) is introduced as a timed and probabilistic actor-based language for modeling distributed real-time systems with asynchronous message passing. The semantics of PTRebeca is a Timed Markov Decision Process (TMDP). We provide SOS rules for PTRebeca, and develop two toolsets for analyzing PTRebeca models. The first toolset automatically generates a TMDP model from a PTRebeca model in the form of the input language of the PRISM model checker. We use PRISM for performance analysis of PTRebeca models against expected reachability and probabilistic reachability properties. Additionally, we develop another toolset to automatically generate a Markov Automaton from a PTRebeca model in the form of the input language of the Interactive Markov Chain Analyzer (IMCA). The IMCA can be used as the back-end model checker for performance analysis of PTRebeca models against expected reachability and probabilistic reachability properties. We present the needed time for the analysis of different case studies using PRISM-based and IMCA-based approaches. The IMCA-based approach needs considerably less time, and so has the ability of analyzing significantly larger models. We show the applicability of both approaches and the efficiency of our tools by analyzing a few case studies and experimental results.Þessi ritgerð er tvískipt. Í fyrri hlutanum er farið í mat og sannprófun á eiginleikum öryggis í rauntímalíkönum. Fyrir stuttu síðan var leikendabyggða málið, Timed Rebeca, notað við líkana dreifingu og ósamstillt kerfi með tímastillingu og samskipti í skilaboðum. Búið var til verkfærasett fyrir sjálfvirka þýðingu á Timed Rebeca líkön yfir í Erlang. Hægt er að nota þýdda kóðann með því að nota tímastillta framlengingu af McErlang fyrir líkanaprófun og hermun. Í fyrri hluta þessarar ritgerðar, ætlum við að kynna verkfærasettið sem veitir tölfræðilega prófun á líkön á Timed Rebeca líkön. Með því að nota tölfræðileg próf á líkön er núna hægt að sannreyna stærri líkön eins og í öryggiskröfum McErlang. Við rannsökum dæmigerðar ferilsathuganir af lyftum og miðasölu til að sýna fram á skilvirkni tölfræðilegra líkana og beitingu verkfærasettsins okkar. Í seinni hluta þessarar ritgerðar aukum við við getu líkanagerðarinnar og við náum yfir fleiri eiginleika með mati á framkvæmd og prófunum á líkönum á líkinda rauntíma leikara. Dreifð kerfi sýna líkindi og brigðgenga hegðun sem kunna að hafa tímamörk. Probabilistic Timed Rebeca (PTRebeca) er kynnt sem tímastillt og líkinda leikarabyggt mál líkindadreifðra rauntímakerfa með ósamstillta sendingu skilaboða. Merkingarfræði PTRebeca er Timed Markov Decision Process (TMDP). Við verðum með SOS reglur fyrir PTRebeca, og þróum tvö verkfærasett til að greina PTRebeca líkön.The work on this dissertation was supported by the project "Timed Asynchronous Reactive Objects in Distributed Systems: TARO" (nr.110020021) of the Icelandic Research Fund

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine