Synthesis for circuit reliability

textElectrical and Computer Engineerin

Incremental volume rendering using hierarchical compression

Includes bibliographical references.The research has been based on the thesis that efficient volume rendering of datasets, contained on the Internet, can be achieved on average personal workstations. We present a new algorithm here for efficient incremental rendering of volumetric datasets. The primary goal of this algorithm is to give average workstations the ability to efficiently render volume data received over relatively low bandwidth network links in such a way that rapid user feedback is maintained. Common limitations of workstation rendering of volume data include: large memory overheads, the requirement of expensive rendering hardware, and high speed processing ability. The rendering algorithm presented here overcomes these problems by making use of the efficient Shear-Warp Factorisation method which does not require specialised graphics hardware. However the original Shear-Warp algorithm suffers from a high memory overhead and does not provide for incremental rendering which is required should rapid user feedback be maintained. Our algorithm represents the volumetric data using a hierarchical data structure which provides for the incremental classification and rendering of volume data. This exploits the multiscale nature of the octree data structure. The algorithm reduces the memory footprint of the original Shear-Warp Factorisation algorithm by a factor of more than two, while maintaining good rendering performance. These factors make our octree algorithm more suitable for implementation on average desktop workstations for the purposes of interactive exploration of volume models over a network. This dissertation covers the theory and practice of developing the octree based Shear-Warp algorithms, and then presents the results of extensive empirical testing. The results, using typical volume datasets, demonstrate the ability of the algorithm to achieve high rendering rates for both incremental rendering and standard rendering while reducing the runtime memory requirements

DCT Implementation on GPU

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 400)

This bibliography lists 397 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April 1995. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Potential of machine learning/Artificial Intelligence (ML/AI) for verifying configurations of 5G multi Radio Access Technology (RAT) base station

Abstract. The enhancements in mobile networks from 1G to 5G have greatly increased data transmission reliability and speed. However, concerns with 5G must be addressed. As system performance and reliability improve, ML and AI integration in products and services become more common. The integration teams in cellular network equipment creation test devices from beginning to end to ensure hardware and software parts function correctly. Radio unit integration is typically the first integration phase, where the radio is tested independently without additional network components like the BBU and UE. 5G architecture and the technology that it is using are explained further. The architecture defined by 3GPP for 5G differs from previous generations, using Network Functions (NFs) instead of network entities. This service-based architecture offers NF reusability to reduce costs and modularity, allowing for the best vendor options for customer radio products. 5G introduced the O-RAN concept to decompose the RAN architecture, allowing for increased speed, flexibility, and innovation. NG-RAN provided this solution to speed up the development and implementation process of 5G. The O-RAN concept aims to improve the efficiency of RAN by breaking it down into components, allowing for more agility and customization. The four protocols, the eCPRI interface, and the functionalities of fronthaul that NGRAN follows are expressed further. Additionally, the significance of NR is described with an explanation of its benefits. Some benefits are high data rates, lower latency, improved spectral efficiency, increased network flexibility, and improved energy efficiency. The timeline for 5G development is provided along with different 3GPP releases. Stand-alone and non-stand-alone architecture is integral while developing the 5G architecture; hence, it is also defined with illustrations. The two frequency bands that NR utilizes, FR1 and FR2, are expressed further. FR1 is a sub-6 GHz frequency band. It contains frequencies of low and high values; on the other hand, FR2 contains frequencies above 6GHz, comprising high frequencies. FR2 is commonly known as the mmWave band. Data collection for implementing the ML approaches is expressed that contains the test setup, data collection, data description, and data visualization part of the thesis work. The Test PC runs tests, executes test cases using test libraries, and collects data from various logs to analyze the system’s performance. The logs contain information about the test results, which can be used to identify issues and evaluate the system’s performance. The data collection part describes that the data was initially present in JSON files and extracted from there. The extraction took place using the Python code script and was then fed into an Excel sheet for further analysis. The data description explains the parameters that are taken while training the models. Jupyter notebook has been used for visualizing the data, and the visualization is carried out with the help of graphs. Moreover, the ML techniques used for analyzing the data are described. In total, three methods are used here. All the techniques come under the category of supervised learning. The explained models are random forest, XG Boost, and LSTM. These three models form the basis of ML techniques applied in the thesis. The results and discussion section explains the outcomes of the ML models and discusses how the thesis will be used in the future. The results include the parameters that are considered to apply the ML models to them. SINR, noise power, rxPower, and RSSI are the metrics that are being monitored. These parameters have variance, which is essential in evaluating the quality of the product test setup, the quality of the software being tested, and the state of the test environment. The discussion section of the thesis explains why the following parameters are taken, which ML model is most appropriate for the data being analyzed, and what the next steps are in implementation

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Etude et mise en place d’une plateforme d’adaptation multiservice embarquée pour la gestion de flux multimédia à différents niveaux logiciels et matériels

On the one hand, technology advances have led to the expansion of the handheld devices market. Thanks to this expansion, people are more and more connected and more and more data are exchanged over the Internet. On the other hand, this huge amound of data imposes drastic constrains in order to achieve sufficient quality. The Internet is now showing its limits to assure such quality. To answer nowadays limitations, a next generation Internet is envisioned. This new network takes into account the content nature (video, audio, ...) and the context (network state, terminal capabilities ...) to better manage its own resources. To this extend, video manipulation is one of the key concept that is highlighted in this arising context. Video content is more and more consumed and at the same time requires more and more resources. Adapting videos to the network state (reducing its bitrate to match available bandwidth) or to the terminal capabilities (screen size, supported codecs, …) appears mandatory and is foreseen to take place in real time in networking devices such as home gateways. However, video adaptation is a resource intensive task and must be implemented using hardware accelerators to meet the desired low cost and real time constraints.In this thesis, content- and context-awareness is first analyzed to be considered at the network side. Secondly, a generic low cost video adaptation system is proposed and compared to existing solutions as a trade-off between system complexity and quality. Then, hardware conception is tackled as this system is implemented in an FPGA based architecture. Finally, this system is used to evaluate the indirect effects of video adaptation; energy consumption reduction is achieved at the terminal side by reducing video characteristics thus permitting an increased user experience for End-Users.Les avancées technologiques ont permis la commercialisation à grande échelle de terminaux mobiles. De ce fait, l’homme est de plus en plus connecté et partout. Ce nombre grandissant d’usagers du réseau ainsi que la forte croissance du contenu disponible, aussi bien d’un point de vue quantitatif que qualitatif saturent les réseaux et l’augmentation des moyens matériels (passage à la fibre optique) ne suffisent pas. Pour surmonter cela, les réseaux doivent prendre en compte le type de contenu (texte, vidéo, ...) ainsi que le contexte d’utilisation (état du réseau, capacité du terminal, ...) pour assurer une qualité d’expérience optimum. A ce sujet, la vidéo fait partie des contenus les plus critiques. Ce type de contenu est non seulement de plus en plus consommé par les utilisateurs mais est aussi l’un des plus contraignant en terme de ressources nécéssaires à sa distribution (taille serveur, bande passante, …). Adapter un contenu vidéo en fonction de l’état du réseau (ajuster son débit binaire à la bande passante) ou des capacités du terminal (s’assurer que le codec soit nativement supporté) est indispensable. Néanmoins, l’adaptation vidéo est un processus qui nécéssite beaucoup de ressources. Cela est antinomique à son utilisation à grande echelle dans les appareils à bas coûts qui constituent aujourd’hui une grande part dans l’ossature du réseau Internet. Cette thèse se concentre sur la conception d’un système d’adaptation vidéo à bas coût et temps réel qui prendrait place dans ces réseaux du futur. Après une analyse du contexte, un système d’adaptation générique est proposé et évalué en comparaison de l’état de l’art. Ce système est implémenté sur un FPGA afin d’assurer les performances (temps-réels) et la nécessité d’une solution à bas coût. Enfin, une étude sur les effets indirects de l’adaptation vidéo est menée

Etude et mise en place d'une plateforme d'adaptation multiservice embarquée pour la gestion de flux multimédia à différents niveaux logiciels et matériels

Les avancées technologiques ont permis la commercialisation à grande échelle de terminaux mobiles. De ce fait, l homme est de plus en plus connecté et partout. Ce nombre grandissant d usagers du réseau ainsi que la forte croissance du contenu disponible, aussi bien d un point de vue quantitatif que qualitatif saturent les réseaux et l augmentation des moyens matériels (passage à la fibre optique) ne suffisent pas. Pour surmonter cela, les réseaux doivent prendre en compte le type de contenu (texte, vidéo, ...) ainsi que le contexte d utilisation (état du réseau, capacité du terminal, ...) pour assurer une qualité d expérience optimum. A ce sujet, la vidéo fait partie des contenus les plus critiques. Ce type de contenu est non seulement de plus en plus consommé par les utilisateurs mais est aussi l un des plus contraignant en terme de ressources nécéssaires à sa distribution (taille serveur, bande passante, ). Adapter un contenu vidéo en fonction de l état du réseau (ajuster son débit binaire à la bande passante) ou des capacités du terminal (s assurer que le codec soit nativement supporté) est indispensable. Néanmoins, l adaptation vidéo est un processus qui nécéssite beaucoup de ressources. Cela est antinomique à son utilisation à grande echelle dans les appareils à bas coûts qui constituent aujourd hui une grande part dans l ossature du réseau Internet. Cette thèse se concentre sur la conception d un système d adaptation vidéo à bas coût et temps réel qui prendrait place dans ces réseaux du futur. Après une analyse du contexte, un système d adaptation générique est proposé et évalué en comparaison de l état de l art. Ce système est implémenté sur un FPGA afin d assurer les performances (temps-réels) et la nécessité d une solution à bas coût. Enfin, une étude sur les effets indirects de l adaptation vidéo est menée.On the one hand, technology advances have led to the expansion of the handheld devices market. Thanks to this expansion, people are more and more connected and more and more data are exchanged over the Internet. On the other hand, this huge amound of data imposes drastic constrains in order to achieve sufficient quality. The Internet is now showing its limits to assure such quality. To answer nowadays limitations, a next generation Internet is envisioned. This new network takes into account the content nature (video, audio, ...) and the context (network state, terminal capabilities ...) to better manage its own resources. To this extend, video manipulation is one of the key concept that is highlighted in this arising context. Video content is more and more consumed and at the same time requires more and more resources. Adapting videos to the network state (reducing its bitrate to match available bandwidth) or to the terminal capabilities (screen size, supported codecs, ) appears mandatory and is foreseen to take place in real time in networking devices such as home gateways. However, video adaptation is a resource intensive task and must be implemented using hardware accelerators to meet the desired low cost and real time constraints.In this thesis, content- and context-awareness is first analyzed to be considered at the network side. Secondly, a generic low cost video adaptation system is proposed and compared to existing solutions as a trade-off between system complexity and quality. Then, hardware conception is tackled as this system is implemented in an FPGA based architecture. Finally, this system is used to evaluate the indirect effects of video adaptation; energy consumption reduction is achieved at the terminal side by reducing video characteristics thus permitting an increased user experience for End-Users.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Lossy and Lossless Compression Techniques to Improve the Utilization of Memory Bandwidth and Capacity

Main memory is a critical resource in modern computer systems and is in increasing demand. An increasing number of on-chip cores and specialized accelerators improves the potential processing throughput but also calls for higher data rates and greater memory capacity. In addition, new emerging data-intensive applications further increase memory traffic and footprint. On the other hand, memory bandwidth is pin limited and power constrained and is therefore more difficult to scale. Memory capacity is limited by cost and energy considerations.This thesis proposes a variety of memory compression techniques as a means to reduce the memory bottleneck. These techniques target two separate problems in the memory hierarchy: memory bandwidth and memory capacity. In order to reduce transferred data volumes, lossy compression is applied which is able to reach more aggressive compression ratios. A reduction of off-chip memory traffic leads to reduced memory latency, which in turn improves the performance and energy efficiency of the system. To improve memory capacity, a novel approach to memory compaction is presented.The first part of this thesis introduces Approximate Value Reconstruction (AVR), which combines a low-complexity downsampling compressor with an LLC design able to co-locate compressed and uncompressed data. Two separate thresholds limit the error introduced by approximation. For applications that tolerate aggressive approximation in large fractions of their data, in a system with 1GB of 1600MHz DDR4 per core and 1MB of LLC space per core, AVR reduces memory traffic by up to 70%, execution time by up to 55%, and energy costs by up to 20% introducing at most 1.2% error in the application output.The second part of this thesis proposes Memory Squeeze (MemSZ), introducing a parallelized implementation of the more advanced Squeeze (SZ) compression method. Furthermore, MemSZ improves on the error limiting capability of AVR by keeping track of life-time accumulated error. An alternate memory compression architecture is also proposed, which utilizes 3D-stacked DRAM as a last-level cache. In a system with 1GB of 800MHz DDR4 per core and 1MB of LLC space per core, MemSZ improves execution time, energy and memory traffic over AVR by up to 15%, 9%, and 64%, respectively.The third part of the thesis describes L2C, a hybrid lossy and lossless memory compression scheme. L2C applies lossy compression to approximable data, and falls back to lossless if an error threshold is exceeded. In a system with 4GB of 800MHz DDR4 per core and 1MB of LLC space per core, L2C improves on the performance of MemSZ by 9%, and energy consumption by 3%.The fourth and final contribution is FlatPack, a novel memory compaction scheme. FlatPack is able to reduce the traffic overhead compared to other memory compaction systems, thus retaining the bandwidth benefits of compression. Furthermore, FlatPack is flexible to changes in block compressibility both over time and between adjacent blocks. When available memory corresponds to 50% of the application footprint, in a system with 4GB of 800MHz DDR4 per core and 1MB of LLC space per core, FlatPack increases system performance compared to current state-of-the-art designs by 36%, while reducing system energy consumption by 12%