Reliability-energy-performance optimisation in combinational circuits in presence of soft errors

PhD ThesisThe reliability metric has a direct relationship to the amount of value produced by a circuit, similar to the performance metric. With advances in CMOS technology, digital circuits become increasingly more susceptible to soft errors. Therefore, it is imperative to be able to assess and improve the level of reliability of these circuits. A framework for evaluating and improving the reliability of combinational circuits is proposed, and an interplay between the metrics of reliability, energy and performance is explored. Reliability evaluation is divided into two levels of characterisation: stochastic fault model (SFM) of the component library and a design-specific critical vector model (CVM). The SFM captures the properties of components with regard to the interference which causes error. The CVM is derived from a limited number of simulation runs on the specific design at the design time and producing the reliability metric. The idea is to move the high-complexity problem of the stochastic characterisation of components to the generic part of the design process, and to do it just once for a large number of specific designs. The method is demonstrated on a range of circuits with various structures. A three-way trade-off between reliability, energy, and performance has been discovered; this trade-off facilitates optimisations of circuits and their operating conditions. A technique for improving the reliability of a circuit is proposed, based on adding a slow stage at the primary output. Slow stages have the ability to absorb narrow glitches from prior stages, thus reducing the error probability. Such stages, or filters, suppress most of the glitches generated in prior stages and prevent them from arriving at the primary output of the circuit. Two filter solutions have been developed and analysed. The results show a dramatic improvement in reliability at the expense of minor performance and energy penalties. To alleviate the problem of the time-consuming analogue simulations involved in the proposed method, a simplification technique is proposed. This technique exploits the equivalence between the properties of the gates within a path and the equivalence between paths. On the basis of these equivalences, it is possible to reduce the number of simulation runs. The effectiveness of the proposed technique is evaluated by applying it to different circuits with a representative variety of path topologies. The results show a significant decrease in the time taken to estimate reliability at the expense of a minor decrease in the accuracy of estimation. The simplification technique enables the use of the proposed method in applications with complex circuits.Ministry of Education and Scientific Research in Liby

Research on performance enhancement for electromagnetic analysis and power analysis in cryptographic LSI

制度:新 ; 報告番号:甲3785号 ; 学位の種類:博士(工学) ; 授与年月日:2012/11/19 ; 早大学位記番号:新6161Waseda Universit

Deep learning methods for enabling real-time gravitational wave and multimessenger astrophysics

A new era of gravitational wave (GW) astronomy has begun with the recent detections by LIGO. However, we need real-time observations of GW signals and their electromagnetic (EM) and astro-particle counterparts to unlock its full potential for scientific discoveries. Extracting and classifying the wide range of modeled and unmodeled GWs, whose amplitudes are often much weaker than the background noise, and rapidly inferring accurate parameters of their source is crucial in enabling this scenario of real-time multimessenger astrophysics. Identifying and automatically clustering anomalous non-Gaussian transient noises (glitches) that frequently contaminate the data and separating them from true GW signals is yet another difficult challenge. Currently, the most sensitive data analysis pipelines are limited by the extreme computational costs of template-matching methods and thus are unable to scale to all types of GW sources and their full parameter space. Accurate numerical models of GW signals covering the entire range of parameters including eccentric and spin-precessing compact binaries, which are essential to infer the astrophysical parameters of an event, are not available. Searches for unmodeled and anomalous signals do not have sufficient sensitivity compared to the targeted searches. Furthermore, existing search pipelines are not optimal for dealing with the non-stationary, non-Gaussian noise in the detectors. This indicates that many critical events will go unnoticed. The primary objective of this thesis is to resolve these issues via deep learning, a state-of-the-art machine learning method based on artificial neural networks. In this thesis we develop robust GW analysis algorithms for analyzing real LIGO/Virgo data based on deep learning with neural networks, that overcomes many limitations of existing techniques, allowing real-time detection and parameter estimation modeled GW sources and unmodeled GW bursts as well as classification and unsupervised clustering of anomalies and glitches in the detectors. This pipeline is designed to be highly scalable, therefore it can be trained with template banks of any size to cover the entire parameter-space of eccentric and spin-precessing black hole binaries as well as other sources and also optimized based on the real-time characteristics of the complex noise in the GW detectors. This deep learning framework may also be extended for low-latency analysis of the raw big data collected across multiple observational instruments to further facilitate real-time multimessenger astrophysics, which promises groundbreaking scientific insights about the origin, evolution, and destiny of the universe. In addition, this work introduces a new paradigm to accelerate scientific discovery by using data derived from high-performance physics simulations on supercomputers to train artificial intelligence algorithms that exploit emerging hardware architectures

Engineering evaluations and studies. Volume 3: Exhibit C

High rate multiplexes asymmetry and jitter, data-dependent amplitude variations, and transition density are discussed

Advancing the search for gravitational waves using machine learning

Over 100 years ago Einstein formulated his now famous theory of General Relativity. In his theory he lays out a set of equations which lead to the beginning of a brand-new astronomical field, Gravitational wave (GW) astronomy. The LIGO-Virgo-KAGRA Collaboration (LVK)’s aim is the detection of GW events from some of the most violent and cataclysmic events in the known universe. The LVK detectors are composed of large-scale Michelson Morley interferometers which are able to detect GWs from a range of sources including: binary black holes (BBHs), binary neutron stars (BNSs), neutron star black holes (NSBHs), supernovae and stochastic GWs. Although these GW events release an incredible amount of energy, the amplitudes of the GWs from such events are also incredibly small. The LVK uses sophisticated techniques such as matched filtering and Bayesian inference in order to both detect and infer source parameters from GW events. Although optimal under many circumstances, these standard methods are computationally expensive to use. Given that the expected number of GW detections by the LVK will be of order 100s in the coming years, there is an urgent need for less computationally expensive detection and parameter inference techniques. A possible solution to reducing the computational expense of such techniques is the exciting field of machine learning (ML). In the first chapter of this thesis, GWs are introduced and it is explained how GWs are detected by the LVK. The sources of GWs are given, as well as methodologies for detecting various source types, such as matched filtering. In addition to GW signal detection techniques, the methods for estimating the parameters of detected GW signals is described (i.e. Bayesian inference). In the second chapter several machine learning algorithms are introduced including: perceptrons, convolutional neural networks (CNNs), autoencoders (AEs), variational autoencoders (VAEs) and conditional variational autoencoders (CVAEs). Practical advice on training/data augmentation techniques is also provided to the reader. In the third chapter, a survey on several ML techniques applied a variety of GW problems are shown. In this thesis, various ML and statistical techniques were deployed such as CVAEs and CNNs in two first-of-their-kind proof-of-principle studies. In the fourth chapter it is described how a CNN may be used to match the sensitivity of matched filtering, the standard technique used by the LVK for detecting GWs. It was shown how a CNN may be trained using simulated BBH waveforms buried in Gaussian noise and signals with Gaussian noise alone. Results of the CNN classification predictions were compared to results from matched filtering given the same testing data as the CNN. In the results it was demonstrated through receiver operating characteristics and efficiency curves that the ML approach is able to achieve the same levels of sensitivity as that of matched filtering. It is also shown that the CNN approach is able to generate predictions in low-latency. Given approximately 25000 GW time series, the CNN is able to produce classification predictions for all 25000 in 1s. In the fifth and sixth chapters, it is shown how CVAEs may be used in order to perform Bayesian inference. A CVAE was trained using simulated BBH waveforms in Gaussian noise, as well as the source parameter values of those waveforms. When testing, the CVAE is only supplied the BBH waveform and is able to produce samples from the Bayesian posterior. Results were compared to that of several standard Bayesian samplers used by the LVK including: Dynesty, ptemcee, emcee, and CPnest. It is shown that when properly trained the CVAE method is able to produce Bayesian posteriors which are consistent with other Bayesian samplers. Results are quantified using a variety of figures of merit such as probability-probability (p-p) plots in order to check the 1-dimensional marginalised posteriors from all approaches are self-consistent with the frequentist perspective. The Jensen—Shannon (JS)-divergence was also employed in order to compute the similarity of different posterior distributions from one another, as well as other figures of merit. It was also demonstrated that the CVAE model was able to produce posteriors with 8000 samples in under a second, representing a 6 order of magnitude increase in performance over traditional sampling methods

Present and Future of Gravitational Wave Astronomy

The first detection on Earth of a gravitational wave signal from the coalescence of a binary black hole system in 2015 established a new era in astronomy, allowing the scientific community to observe the Universe with a new form of radiation for the first time. More than five years later, many more gravitational wave signals have been detected, including the first binary neutron star coalescence in coincidence with a gamma ray burst and a kilonova observation. The field of gravitational wave astronomy is rapidly evolving, making it difficult to keep up with the pace of new detector designs, discoveries, and astrophysical results. This Special Issue is, therefore, intended as a review of the current status and future directions of the field from the perspective of detector technology, data analysis, and the astrophysical implications of these discoveries. Rather than presenting new results, the articles collected in this issue will serve as a reference and an introduction to the field. This Special Issue will include reviews of the basic properties of gravitational wave signals; the detectors that are currently operating and the main sources of noise that limit their sensitivity; planned upgrades of the detectors in the short and long term; spaceborne detectors; a data analysis of the gravitational wave detector output focusing on the main classes of detected and expected signals; and implications of the current and future discoveries on our understanding of astrophysics and cosmology

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Timing Signals and Radio Frequency Distribution Using Ethernet Networks for High Energy Physics Applications

Timing networks are used around the world in various applications from telecommunications systems to industrial processes, and from radio astronomy to high energy physics. Most timing networks are implemented using proprietary technologies at high operation and maintenance costs. This thesis presents a novel timing network capable of distributed timing with subnanosecond accuracy. The network, developed at CERN and codenamed “White- Rabbit”, uses a non-dedicated Ethernet link to distribute timing and data packets without infringing the sub-nanosecond timing accuracy required for high energy physics applications. The first part of this thesis proposes a new digital circuit capable of measuring time differences between two digital clock signals with sub-picosecond time resolution. The proposed digital circuit measures and compensates for the phase variations between the transmitted and received network clocks required to achieve the sub-nanosecond timing accuracy. Circuit design, implementation and performance verification are reported. The second part of this thesis investigates and proposes a new method to distribute radio frequency (RF) signals over Ethernet networks. The main goal of existing distributed RF schemes, such as Radio-Over-Fibre or Digitised Radio-Over-Fibre, is to increase the bandwidth capacity taking advantage of the higher performance of digital optical links. These schemes tend to employ dedicated and costly technologies, deemed unnecessary for applications with lower bandwidth requirements. This work proposes the distribution of RF signals over the “White-Rabbit” network, to convey phase and frequency information from a reference base node to a large numbers of remote nodes, thus achieving high performance and cost reduction of the timing network. Hence, this thesis reports the design and implementation of a new distributed RF system architecture; analysed and tested using a purpose-built simulation environment, with results used to optimise a new bespoke FPGA implementation. The performance is evaluated through phase-noise spectra, the Allan-Variance, and signalto- noise ratio measurements of the distributed signals

Low power circuits and systems for wireless neural stimulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-161).Electrical stimulation of tissues is an increasingly valuable tool for treating a variety of disorders, with applications including cardiac pacemakers, cochlear implants, visual prostheses, deep brain stimulators, spinal cord stimulators, and muscle stimulators. Brain implants for paralysis treatments are increasingly providing sensory feedback via neural stimulation. Within the field of neuroscience, the perturbation of neuronal circuits wirelessly in untethered, freely-behaving animals is of particular importance. In implantable systems, power consumption is often the limiting factor in determining battery or power coil size, cost, and level of tissue heating, with stimulation circuitry typically dominating the power budget of the entire implant. Thus, there is strong motivation to improve the energy efficiency of implantable electrical stimulators. In this thesis, I present two examples of low-power tissue stimulators. The first type is a wireless, low-power neural stimulation system for use in freely behaving animals. The system consists of an external transmitter and a miniature, implantable wireless receiver-and-stimulator utilizing a custom integrated chip built in a standard 0.5 ptm CMOS process. Low power design permits 12 days of continuous experimentation from a 5 mAh battery, extended by an automatic sleep mode that reduces standby power consumption by 2.5x. To test this device, bipolar stimulating electrodes were implanted into the songbird motor nucleus HVC of zebra finches. Single-neuron recordings revealed that wireless stimulation of HVC led to a strong increase of spiking activity in its downstream target, the robust nucleus of the arcopallium (RA). When this device was used to deliver biphasic pulses of current randomly during singing, singing activity was prematurely terminated in all birds tested. The second stimulator I present is a novel, energy-efficient electrode stimulator with feedback current regulation. This stimulator uses inductive storage and recycling of energy based on a dynamic power supply to drive an electrode in an adiabatic fashion such that energy consumption is minimized. Since there are no explicit current sources or current limiters, wasteful energy dissipation across such elements is naturally avoided. The stimulator also utilizes a shunt current-sensor to monitor and regulate the current through the electrode via feedback, thus enabling flexible and safe stimulation. The dynamic power supply allows efficient transfer of energy both to and from the electrode, and is based on a DC-DC converter topology that is used in a bidirectional fashion. In an exemplary electrode implementation, I show how the stimulator combines the efficiency of voltage control and the safety and accuracy of current control in a single low-power integrated-circuit built in a standard 0.35 pm CMOS process. I also perform a theoretical analysis of the energy efficiency that is in accord with experimental measurements. In its current proof-of-concept implementation, this stimulator achieves a 2x-3x reduction in energy consumption as compared to a conventional current-source-based stimulator operating from a fixed power supply.by Scott Kenneth Arfin.Ph.D

Automatic processing of local earthquake data

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Earth and Planetary Sciences, 1979.Microfiche copy available in Archives and Science.Bibliography: leaves 163-173.by Kenneth Robert Anderson.Ph.D