14 research outputs found
Miniaturized embedded stereo vision system (MESVS)
Stereo vision is one of the fundamental problems of computer vision. It is also one of the oldest and heavily investigated areas of 3D vision. Recent advances of stereo matching methodologies and availability of high performance and efficient algorithms along with availability of fast and affordable hardware technology, have allowed researchers to develop several stereo vision systems capable of operating at real-time. Although a multitude of such systems exist in the literature, the majority of them concentrates only on raw performance and quality rather than factors such as dimension, and power requirement, which are of significant importance in the embedded settings.
In this thesis a new miniaturized embedded stereo vision system (MESVS) is presented, which is miniaturized to fit within a package of 5x5cm, is power efficient, and cost-effective. Furthermore, through application of embedded programming techniques and careful optimization, MESVS achieves the real-time performance of 20 frames per second. This work discusses the various challenges involved regarding design and implementation of this system and the measures taken to tackle them
Gbit/second lossless data compression hardware
This thesis investigates how to improve the performance of lossless data compression hardware
as a tool to reduce the cost per bit stored in a computer system or transmitted over a
communication network.
Lossless data compression allows the exact reconstruction of the original data after
decompression. Its deployment in some high-bandwidth applications has been hampered due to
performance limitations in the compressing hardware that needs to match the performance of the
original system to avoid becoming a bottleneck. Advancing the area of lossless data compression
hardware, hence, offers a valid motivation with the potential of doubling the performance of the
system that incorporates it with minimum investment.
This work starts by presenting an analysis of current compression methods with the objective of
identifying the factors that limit performance and also the factors that increase it. [Continues.
Design and Code Optimization for Systems with Next-generation Racetrack Memories
With the rise of computationally expensive application domains such as machine learning, genomics, and fluids simulation, the quest for performance and energy-efficient computing has gained unprecedented momentum. The significant increase in computing and memory devices in modern systems has resulted in an unsustainable surge in energy consumption, a substantial portion of which is attributed to the memory system. The scaling of conventional memory technologies and their suitability for the next-generation system is also questionable. This has led to the emergence and rise of nonvolatile memory ( NVM ) technologies. Today, in different development stages, several NVM technologies are competing for their rapid access to the market.
Racetrack memory ( RTM ) is one such nonvolatile memory technology that promises SRAM -comparable latency, reduced energy consumption, and unprecedented density compared to other technologies. However, racetrack memory ( RTM ) is sequential in nature, i.e., data in an RTM cell needs to be shifted to an access port before it can be accessed. These shift operations incur performance and energy penalties. An ideal RTM , requiring at most one shift per access, can easily outperform SRAM . However, in the worst-cast shifting scenario, RTM can be an order of magnitude slower than SRAM .
This thesis presents an overview of the RTM device physics, its evolution, strengths and challenges, and its application in the memory subsystem. We develop tools that allow the programmability and modeling of RTM -based systems. For shifts minimization, we propose a set of techniques including optimal, near-optimal, and evolutionary algorithms for efficient scalar and instruction placement in RTMs . For array accesses, we explore schedule and layout transformations that eliminate the longer overhead shifts in RTMs . We present an automatic compilation framework that analyzes static control flow programs and transforms the loop traversal order and memory layout to maximize accesses to consecutive RTM locations and minimize shifts. We develop a simulation framework called RTSim that models various RTM parameters and enables accurate architectural level simulation.
Finally, to demonstrate the RTM potential in non-Von-Neumann in-memory computing paradigms, we exploit its device attributes to implement logic and arithmetic operations. As a concrete use-case, we implement an entire hyperdimensional computing framework in RTM to accelerate the language recognition problem. Our evaluation shows considerable performance and energy improvements compared to conventional Von-Neumann models and state-of-the-art accelerators
A novel approach for the hardware implementation of a PPMC statistical data compressor
This thesis aims to understand how to design high-performance compression
algorithms suitable for hardware implementation and to provide hardware support for
an efficient compression algorithm.
Lossless data compression techniques have been developed to exploit the available
bandwidth of applications in data communications and computer systems by reducing
the amount of data they transmit or store. As the amount of data to handle is ever
increasing, traditional methods for compressing data become· insufficient. To
overcome this problem, more powerful methods have been developed. Among those
are the so-called statistical data compression methods that compress data based on
their statistics. However, their high complexity and space requirements have prevented
their hardware implementation and the full exploitation of their potential benefits.
This thesis looks into the feasibility of the hardware implementation of one of these
statistical data compression methods by exploring the potential for reorganising and
restructuring the method for hardware implementation and investigating ways of
achieving efficient and effective designs to achieve an efficient and cost-effective
algorithm. [Continues.
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
Remote Sensing Data Compression
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin
Concepts enacted: confronting the obstacles and paradoxes inherent in pursuing a scientific understanding of the building blocks of human thought
This thesis confronts a fundamental shortcoming in cognitive science research: a failure to be explicit about the theory of concepts underlying cognitive science research and a resulting failure to justify that theory philosophically or otherwise. It demonstrates how most contemporary debates over theories of concepts divide over whether concepts are best understood as (mental) representations or as non-representational abilities. It concludes that there can be no single correct ontology, and that both perspectives are logically necessary. It details three critical distinctions that are frequently neglected: between concepts as we possess and employ them non-reflectively, and concepts as we reflect upon them; between the private (subjective) and public (inter-subjective) aspects of concepts; and between concepts as approached from a realist versus anti-realist perspective. Metaphysical starting points fundamentally shape conclusions.
The main contribution of this thesis is a pragmatic, meticulously detailed, and distinctive account of concepts in terms of their essential nature, core properties, and context of application. This is done within the framework of Peter Gärdenfors’ conceptual spaces theory of concepts, which is offered as a bridging account, best able to tie existing theories together into one framework. A set of extensions to conceptual spaces theory, called the unified conceptual space theory, are offered as a means of pushing Gärdenfors’ theory in a more algorithmically amenable and empirically testable direction. The unified conceptual space theory describes how all of an agent’s many different conceptual spaces, as described by Gärdenfors, are mapped together into one unified space of spaces, and how an analogous process happens at the societal level.
The unified conceptual space theory is put to work offering a distinctive account of the co-emergence of concepts and experience out of a circularly causal process. Finally, an experimental application of the theory is presented, in the form of a simple computer program
Data and the city – accessibility and openness. a cybersalon paper on open data
This paper showcases examples of bottom–up open data and smart city applications and identifies lessons for future such efforts. Examples include Changify, a neighbourhood-based platform for residents, businesses, and companies; Open Sensors, which provides APIs to help businesses, startups, and individuals develop applications for the Internet of Things; and Cybersalon’s Hackney Treasures. a location-based mobile app that uses Wikipedia entries geolocated in Hackney borough to map notable local residents. Other experiments with sensors and open data by Cybersalon members include Ilze Black and Nanda Khaorapapong's The Breather, a "breathing" balloon that uses high-end, sophisticated sensors to make air quality visible; and James Moulding's AirPublic, which measures pollution levels. Based on Cybersalon's experience to date, getting data to the people is difficult, circuitous, and slow, requiring an intricate process of leadership, public relations, and perseverance. Although there are myriad tools and initiatives, there is no one solution for the actual transfer of that data