Memristive Computing

Memristive computing refers to the utilization of the memristor, the fourth fundamental passive circuit element, in computational tasks. The existence of the memristor was theoretically predicted in 1971 by Leon O. Chua, but experimentally validated only in 2008 by HP Labs. A memristor is essentially a nonvolatile nanoscale programmable resistor — indeed, memory resistor — whose resistance, or memristance to be precise, is changed by applying a voltage across, or current through, the device. Memristive computing is a new area of research, and many of its fundamental questions still remain open. For example, it is yet unclear which applications would benefit the most from the inherent nonlinear dynamics of memristors. In any case, these dynamics should be exploited to allow memristors to perform computation in a natural way instead of attempting to emulate existing technologies such as CMOS logic. Examples of such methods of computation presented in this thesis are memristive stateful logic operations, memristive multiplication based on the translinear principle, and the exploitation of nonlinear dynamics to construct chaotic memristive circuits. This thesis considers memristive computing at various levels of abstraction. The first part of the thesis analyses the physical properties and the current-voltage behaviour of a single device. The middle part presents memristor programming methods, and describes microcircuits for logic and analog operations. The final chapters discuss memristive computing in largescale applications. In particular, cellular neural networks, and associative memory architectures are proposed as applications that significantly benefit from memristive implementation. The work presents several new results on memristor modeling and programming, memristive logic, analog arithmetic operations on memristors, and applications of memristors. The main conclusion of this thesis is that memristive computing will be advantageous in large-scale, highly parallel mixed-mode processing architectures. This can be justified by the following two arguments. First, since processing can be performed directly within memristive memory architectures, the required circuitry, processing time, and possibly also power consumption can be reduced compared to a conventional CMOS implementation. Second, intrachip communication can be naturally implemented by a memristive crossbar structure.

Differential Evolution approach and parameter estimation of chaotic

Parameter estimation still remains a challenge in many important applications. There is a need to develop methods that utilize achievements in modern computational systems with growing capabilities. Owing to this fact different kinds of Evolutionary Algorithms are becoming an especially perspective field of research. The main aim of this thesis is to explore theoretical aspects of a specific type of Evolutionary Algorithms class, the Differential Evolution (DE) method, and implement this algorithm as codes capable to solve a large range of problems. Matlab, a numerical computing environment provided by MathWorks inc., has been utilized for this purpose. Our implementation empirically demonstrates the benefits of a stochastic optimizers with respect to deterministic optimizers in case of stochastic and chaotic problems. Furthermore, the advanced features of Differential Evolution are discussed as well as taken into account in the Matlab realization. Test "toycase" examples are presented in order to show advantages and disadvantages caused by additional aspects involved in extensions of the basic algorithm. Another aim of this paper is to apply the DE approach to the parameter estimation problem of the system exhibiting chaotic behavior, where the well-known Lorenz system with specific set of parameter values is taken as an example. Finally, the DE approach for estimation of chaotic dynamics is compared to the Ensemble prediction and parameter estimation system (EPPES) approach which was recently proposed as a possible solution for similar problems.

Near-Threshold Computing

Valmistustekniikoiden kehittyessä IC-piireille saadaan mahtumaan yhä enemmän transistoreja. Monimutkaisemmat piirit mahdollistavat suurempien laskutoimitusmäärien suorittamisen aikayksikössä. Piirien aktiivisuuden lisääntyessä myös niiden energiankulutus lisääntyy, ja tämä puolestaan lisää piirin lämmöntuotantoa. Liiallinen lämpö rajoittaa piirien toimintaa. Tämän takia tarvitaan tekniikoita, joilla piirien energiankulutusta saadaan pienennettyä. Uudeksi tutkimuskohteeksi ovat tulleet pienet laitteet, jotka seuraavat esimerkiksi ihmiskehon toimintaa, rakennuksia tai siltoja. Tällaisten laitteiden on oltava energiankulutukseltaan pieniä, jotta ne voivat toimia pitkiä aikoja ilman akkujen lataamista. Near-Threshold Computing on tekniikka, jolla pyritään pienentämään integroitujen piirien energiankulutusta. Periaatteena on käyttää piireillä pienempää käyttöjännitettä kuin piirivalmistaja on niille alunperin suunnitellut. Tämä hidastaa ja haittaa piirin toimintaa. Jos kuitenkin laitteen toiminnassa pystyään hyväksymään huonompi laskentateho ja pienentynyt toimintavarmuus, voidaan saavuttaa säästöä energiankulutuksessa. Tässä diplomityössä tarkastellaan Near-Threshold Computing -tekniikkaa eri näkökulmista: aluksi perustuen kirjallisuudesta löytyviin aikaisempiin tutkimuksiin, ja myöhemmin tutkimalla Near-Threshold Computing -tekniikan soveltamista kahden tapaustutkimuksen kautta. Tapaustutkimuksissa tarkastellaan FO4-invertteriä sekä 6T SRAM -solua piirisimulaatioiden avulla. Näiden komponenttien käyttäytymisen Near-Threshold Computing –jännitteillä voidaan tulkita antavan kattavan kuvan suuresta osasta tavanomaisen IC-piirin pinta-alaa ja energiankulusta. Tapaustutkimuksissa käytetään 130 nm teknologiaa, ja niissä mallinnetaan todellisia piirivalmistusprosessin tuotteita ajamalla useita Monte Carlo -simulaatioita. Tämä valmistuskustannuksiltaan huokea teknologia yhdistettynä Near-Threshold Computing -tekniikkaan mahdollistaa matalan energiankulutuksen piirien valmistaminen järkevään hintaan. Tämän diplomityön tulokset näyttävät, että Near-Threshold Computing pienentää piirien energiankulutusta merkittävästi. Toisaalta, piirien nopeus heikkenee, ja yleisesti käytetty 6T SRAM -muistisolu muuttuu epäluotettavaksi. Pidemmät polut logiikkapiireissä sekä transistorien kasvattaminen muistisoluissa osoitetaan tehokkaiksi vastatoimiksi Near- Threshold Computing -tekniikan huonoja puolia vastaan. Tulokset antavat perusteita matalan energiankulutuksen IC-piirien suunnittelussa sille, kannattaako käyttää normaalia käyttöjännitettä, vai laskea sitä, jolloin piirin hidastuminen ja epävarmempi käyttäytyminen pitää ottaa huomioon.

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.

What are the opportunities and challenges of cloud computing technology in the healthcare information systems

This thesis discusses the opportunities and challenges of the cloud computing technology in healthcare information systems by reviewing the existing literature on cloud computing and healthcare information system and the impact of cloud computing technology to healthcare industry. The review shows that if problems related to security of data are solved then cloud computing will positively transform the healthcare institutions by giving advantage to the healthcare IT infrastructure as well as improving and giving benefit to healthcare services. Therefore, this thesis will explore the opportunities and challenges that are associated with cloud computing in the context of Finland in order to help the healthcare organizations and stakeholders to determine its direction when it decides to adopt cloud technology on their information systems.

Video transcoding in a distributed cloud computing environment

Video transcoding refers to the process of converting a digital video from one format into another format. It is a compute-intensive operation. Therefore, transcoding of a large number of simultaneous video streams requires a large amount of computing resources. Moreover, to handle di erent load conditions in a cost-e cient manner, the video transcoding service should be dynamically scalable. Infrastructure as a Service Clouds currently offer computing resources, such as virtual machines, under the pay-per-use business model. Thus the IaaS Clouds can be leveraged to provide a coste cient, dynamically scalable video transcoding service. To use computing resources e ciently in a cloud computing environment, cost-e cient virtual machine provisioning is required to avoid overutilization and under-utilization of virtual machines. This thesis presents proactive virtual machine resource allocation and de-allocation algorithms for video transcoding in cloud computing. Since users' requests for videos may change at di erent times, a check is required to see if the current computing resources are adequate for the video requests. Therefore, the work on admission control is also provided. In addition to admission control, temporal resolution reduction is used to avoid jitters in a video. Furthermore, in a cloud computing environment such as Amazon EC2, the computing resources are more expensive as compared with the storage resources. Therefore, to avoid repetition of transcoding operations, a transcoded video needs to be stored for a certain time. To store all videos for the same amount of time is also not cost-e cient because popular transcoded videos have high access rate while unpopular transcoded videos are rarely accessed. This thesis provides a cost-e cient computation and storage trade-o strategy, which stores videos in the video repository as long as it is cost-e cient to store them. This thesis also proposes video segmentation strategies for bit rate reduction and spatial resolution reduction video transcoding. The evaluation of proposed strategies is performed using a message passing interface based video transcoder, which uses a coarse-grain parallel processing approach where video is segmented at group of pictures level.

Incest aversion : the evolutionary roots of individual regulation

Den evolutionära förklaringen till den allmänt utbredda incestaversionen, dvs. motviljan för sex med nära släktingar, försöker besvara såväl frågan om varför incestaversionen gynnats i det naturliga urvalet som frågan om hur denna aversion regleras på individuell nivå. Eftersom inavlade barn har en försämrad biologisk duglighet än andra barn, medför kostnaderna av denna icke-optimala reproduktion ett selektionstryck mot inavel. I en serie studier visade vi att eftersom kvinnor i allmänhet satsar mer biologiska resurser på sina barn än vad män gör, känner kvinnor starkare incestaversion än män och eftersom det endast är fertila kvinnor som riskerar satsa resurser i en inavlad avkomma har även fertila kvinnor högre incestaversion än icke-fertila kvinnor. Vi visade också att de biologiska kostnaderna av inavel inte begränsas till enbart de individer som har incest. Eftersom alla våra biologiska släktingar sannolikt delar våra alleler speglar incestaversionen även de biologiska kostnader som incest mellan våra släktingar medför åt oss. Den psykologiska mekanism med vilken incestsituationer bedöms har hittills varit okänd. I våra studier testades teorin om självreflekterande empati. Enligt den teorin bedöms sådana situationer emotionellt genom att man själv föreställer sig ha sex med motsvarande släkting och känslan som väcks i denna process ger därefter emotionell information till bedömningen av andras incest. I tre delstudier fann vi att självreflektion var positivt associerat med styrkan av aversion gentemot andras incest, vilket stöder teorin om självreflekterande empati.

Achieving EU energy targets – Impacts of evolutionary and revolutionary transition paths on power systems

Ambitious energy targets set by EU put pressures to increase share of renewable electricity supply in this and next decades and therefore, some EU member countries have boosted increasing renewable energy generation capacity by implementing subsidy schemes on national level. In this study, two different change approaches to increase renewable energy supply and increase self-sufficiency of supply are assessed with respect to their impacts on power system, electricity market and electricity generation costs in Finland. It is obtained that the current electricity generation costs are high compared to opportunities of earnings from present-day investor’s perspective. In addition, the growth expectations of consumptions and the price forecasts do not stimulate investing in new generation capacity. Revolutionary transition path is driven by administrative and political interventions to achieve the energy targets. Evolutionary transition path is driven by market-based mechanisms, such as market itself and emission trading scheme. It is obtained in this study that in the revolutionary transition path operation of market-based mechanisms is distorted to some extent and it is likely that this path requires providing more public financial resources compared to evolutionary transition path. In the evolutionary transition path the energy targets are not achieved as quickly but market-based mechanisms function better and investment environment endures more stable compared to revolutionary transition path.

Development of agnostic mobile applications with cross-platform cloud computing platforms

Smart phones became part and parcel of our life, where mobility provides a freedom of not being bounded by time and space. In addition, number of smartphones produced each year is skyrocketing. However, this also created discrepancies or fragmentation among devices and OSes, which in turn made an exceeding hard for developers to deliver hundreds of similar featured applications with various versions for the market consumption. This thesis is an attempt to investigate whether cloud based mobile development platforms can mitigate and eventually eliminate fragmentation challenges. During this research, we have selected and analyzed the most popular cloud based development platforms and tested integrated cloud features. This research showed that cloud based mobile development platforms may able to reduce mobile fragmentation and enable to utilize single codebase to deliver a mobile application for different platforms.

Analyzing and Computing the Sustainability Gains of Building Automation

Smart home implementation in residential buildings promises to optimize energy usage and save significant amount of energy simply due to a better understanding of user's energy usage profile. Apart from the energy optimisation prospects of this technology, it also aims to guarantee occupants significant amount of comfort and remote control over home appliances both at home locations and at remote places. However, smart home investment just like any other kind of investment requires an adequate measurement and justification of the economic gains it could proffer before its realization. These economic gains could differ for different occupants due to their inherent behaviours and tendencies. Thus it is pertinent to investigate the various behaviours and tendencies of occupants in different domain of interests and to measure the value of the energy savings accrued by smart home implementations in these domains of interest in order to justify such economic gains. This thesis investigates two domains of interests (the rented apartment and owned apartment) for primarily two behavioural tendencies (Finland and Germany) obtained from observation and corroborated by conducted interviews to measure the payback time and Return on Investment (ROI) of their smart home implementations. Also, similar measures are obtained for identified Australian use case. The research finding reveals that building automation for the Finnish behavioural tendencies seems to proffers a better ROI and payback time for smart home implementations.

Power Consumption Measurement and Scenario Based Energy Model in Wearable Computing Applications

The power is still today an issue in wearable computing applications. The aim of the present paper is to raise awareness of the power consumption of wearable computing devices in specific scenarios to be able in the future to design energy efficient wireless sensors for context recognition in wearable computing applications. The approach is based on a hardware study. The objective of this paper is to analyze and compare the total power consumption of three representative wearable computing devices in realistic scenarios such as Display, Speaker, Camera and microphone, Transfer by Wi-Fi, Monitoring outdoor physical activity and Pedometer. A scenario based energy model is also developed. The Samsung Galaxy Nexus I9250 smartphone, the Vuzix M100 Smart Glasses and the SimValley Smartwatch AW-420.RX are the three devices representative of their form factors. The power consumption is measured using PowerTutor, an android energy profiler application with logging option and using unknown parameters so it is adjusted with the USB meter. The result shows that the screen size is the main parameter influencing the power consumption. The power consumption for an identical scenario varies depending on the wearable devices meaning that others components, parameters or processes might impact on the power consumption and further study is needed to explain these variations. This paper also shows that different inputs (touchscreen is more efficient than buttons controls) and outputs (speaker sensor is more efficient than display sensor) impact the energy consumption in different way. This paper gives recommendations to reduce the energy consumption in healthcare wearable computing application using the energy model.

Cloud computing contribution to manufacturing industry

Manufacturing industry has been always facing challenge to improve the production efficiency, product quality, innovation ability and struggling to adopt cost-effective manufacturing system. In recent years cloud computing is emerging as one of the major enablers for the manufacturing industry. Combining the emerged cloud computing and other advanced manufacturing technologies such as Internet of Things, service-oriented architecture (SOA), networked manufacturing (NM) and manufacturing grid (MGrid), with existing manufacturing models and enterprise information technologies, a new paradigm called cloud manufacturing is proposed by the recent literature. This study presents concepts and ideas of cloud computing and cloud manufacturing. The concept, architecture, core enabling technologies, and typical characteristics of cloud manufacturing are discussed, as well as the difference and relationship between cloud computing and cloud manufacturing. The research is based on mixed qualitative and quantitative methods, and a case study. The case is a prototype of cloud manufacturing solution, which is software platform cooperated by ATR Soft Oy and SW Company China office. This study tries to understand the practical impacts and challenges that are derived from cloud manufacturing. The main conclusion of this study is that cloud manufacturing is an approach to achieve the transformation from traditional production-oriented manufacturing to next generation service-oriented manufacturing. Many manufacturing enterprises are already using a form of cloud computing in their existing network infrastructure to increase flexibility of its supply chain, reduce resources consumption, the study finds out the shift from cloud computing to cloud manufacturing is feasible. Meanwhile, the study points out the related theory, methodology and application of cloud manufacturing system are far from maturity, it is still an open field where many new technologies need to be studied.