Just-in-time Hardware generation for abstracted reconfigurable computing

This thesis addresses the use of reconfigurable hardware in computing platforms, in order to harness the performance benefits of dedicated hardware whilst maintaining the flexibility associated with software. Although the reconfigurable computing concept is not new, the low level nature of the supporting tools normally used, together with the consequent limited level of abstraction and resultant lack of backwards compatibility, has prevented the widespread adoption of this technology. In addition, bandwidth and architectural limitations, have seriously constrained the potential improvements in performance. A review of existing approaches and tools flows is conducted to highlight the current problems being faced in this field. The objective of the work presented in this thesis is to introduce a radically new approach to reconfigurable computing tool flows. The runtime based tool flow introduces complete abstraction between the application developer and the underlying hardware. This new technique eliminates the ease of use and backwards compatibility issues that have plagued the reconfigurable computing concept, and could pave the way for viable mainstream reconfigurable computing platforms. An easy to use, cycle accurate behavioural modelling system is also presented, which was used extensively during the early exploration of new concepts and architectures. Some performance improvements produced by the new reconfigurable computing tool flow, when applied to both a MIPS based embedded platform, and the Cray XDl, are also presented. These results are then analyzed and the hardware and software factors affecting the performance increases that were obtained are discussed, together with potential techniques that could be used to further increase the performance of the system. Lastly a heterogenous computing concept is proposed, in which, a computer system, containing multiple types of computational resource is envisaged, each having their own strengths and weaknesses (e.g. DSPs, CPUs, FPGAs). A revolutionary new method of fully exploiting the potential of such a system, whilst maintaining scalability, backwards compatibility, and ease of use is also presented

Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

SInCom 2015

2nd Baden-Württemberg Center of Applied Research Symposium on Information and Communication Systems, SInCom 2015, 13. November 2015 in Konstan

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

Tiled microprocessors

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 251-258).Current-day microprocessors have reached the point of diminishing returns due to inherent scalability limitations. This thesis examines the tiled microprocessor, a class of microprocessor which is physically scalable but inherits many of the desirable properties of conventional microprocessors. Tiled microprocessors are composed of an array of replicated tiles connected by a special class of network, the Scalar Operand Network (SON), which is optimized for low-latency, low-occupancy communication between remote ALUs on different tiles. Tiled microprocessors can be constructed to scale to 100's or 1000's of functional units. This thesis identifies seven key criteria for achieving physical scalability in tiled microprocessors. It employs an archetypal tiled microprocessor to examine the challenges in achieving these criteria and to explore the properties of Scalar Operand Networks. The thesis develops the field of SONs in three major ways: it introduces the 5-tuple performance metric, it describes a complete, high-frequency SON implementation, and it proposes a taxonomy, called AsTrO, for categorizing them.(cont.) To develop these ideas, the thesis details the design, implementation and analysis of a tiled microprocessor prototype, the Raw Microprocessor, which was implemented at MIT in 180 nm technology. Overall, compared to Raw, recent commercial processors with half the transistors required 30x as many lines of code, occupied 100x as many designers, contained 50x as many pre-tapeout bugs, and resulted in 33x as many post-tapeout bugs. At the same time, the Raw microprocessor proves to be more versatile in exploiting ILP, stream, and server-farm workloads with modest to large amounts of parallelism.by Michael Bedford Taylor.Ph.D

Implementation and Algorithm Development of 3D ARFI and SWEI Imaging for in vivo Detection of Prostate Cancer

<p>Prostate cancer (PCa) is the most common non-cutaneous cancer in men with an estimated almost 30,000 deaths occurring in the United States in 2014. Currently, the most widely utilized methods for screening men for prostate cancer include the digital rectal exam and prostate specific antigen analysis; however, these methods lack either high sensitivity or specificity, requiring needle biopsy to confirm the presence of cancer. The biopsies are conventionally performed with only B-mode ultrasound visualization of the organ and no targeting of specific regions of the prostate, although recently, multi-parametric magnetic resonance imaging has shown promise for targeting biopsies. Earlier work has demonstrated the feasibility of acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) to visualize cancer in the prostate, however multiple challenges with both methods have been identified.</p><p>The aim of this thesis is to contribute to both the technical development and clinical applications of ARFI and SWEI imaging using the latest advancements in ultrasound imaging technology.</p><p>The introduction of the Siemens Acuson SC2000 provided multiple technological improvements over previous generations of ultrasound scanners, including: an improved power supply, arbitrary waveform generator, and additional parallel receive beamforming. In this thesis, these capabilities were utilized to improve both ARFI and SWEI imaging and reduce acoustic exposure and acquisition duration. However, the SC2000 did not originally have radiation force imaging capabilities; therefore, a new tool set for prototyping these sequences was developed along with rapid data processing and display code. These tools leveraged the increasing availability of general purpose computing on graphics processing units (GPUs) to significantly reduce the data processing time, facilitating real-time display for ultrasonic research systems.</p><p>These technical developments for both acquisition and processing were applied to investigate new methods for ARFI and SWEI imaging. Specifically, the power supply on the SC2000 allowed for a new type of multi-focal zone ARFI images to be acquired, which are shown to provide improved image quality over an extended depth of field. Additionally, a new algorithm for SWEI image processing was developed using an adaptive filter based on a maximum a posteriori estimator, demonstrating increases in the contrast to noise ratio of lesion targets upwards of 50%.</p><p>Finally, the optimized ARFI imaging methods were integrated with a transrectal ultrasound transducer to acquire volumetric in vivo data in patients undergoing robotic radical prostatectomy procedures in an ongoing study. When the study was initiated, it was recognized that the technological improvements of Siemens Acuson SC2000 allowed for the off-axis response to the radiation force excitation to be concurrently recorded without impacting ARFI image quality. This volumetric SWEI data was reconstructed retrospectively using the approaches developed in this thesis, but the images were low quality. A further investigation identified multiple challenges with the SWEI sequence, which should be addressed in future studies. The ARFI image volumes were very high quality and are currently being analyzed to assess the accuracy of ARFI to visualize prostate anatomy and clinically significant prostate cancer tumors. After a blinded evaluation of the ARFI image volumes for suspicion of prostate cancer, three readers correctly identified 63% of all clinically significant tumors and 74% of clinically significant tumors in the posterior region, showing great promise for using ARFI in the context of prostate cancer visualization for targeting biopsies, focal therapy, and watchful waiting.</p>Dissertatio

An Energy-Efficient and Reliable Data Transmission Scheme for Transmitter-based Energy Harvesting Networks

Energy harvesting technology has been studied to overcome a limited power resource problem for a sensor network. This paper proposes a new data transmission period control and reliable data transmission algorithm for energy harvesting based sensor networks. Although previous studies proposed a communication protocol for energy harvesting based sensor networks, it still needs additional discussion. Proposed algorithm control a data transmission period and the number of data transmission dynamically based on environment information. Through this, energy consumption is reduced and transmission reliability is improved. The simulation result shows that the proposed algorithm is more efficient when compared with previous energy harvesting based communication standard, Enocean in terms of transmission success rate and residual energy.This research was supported by Basic Science Research Program through the National Research Foundation by Korea (NRF) funded by the Ministry of Education, Science and Technology(2012R1A1A3012227)