249 research outputs found
Towards autonomic virtual machine management
No full textVirtual machine technologies are gaining wide acceptance in today’s era due to invaluable services in system management, server consolidation, and secure resource containment along with providing requisite application execution environment. Every virtual machine platform reduces dependence on hardware by fully or partially abstracting operating systems enabling flexible control of manipulation or migration of guest machines by manual system administration or reactive/proactive approaches to management. This dissertation focuses on resolving the resource reservation problem to help define a mathematical model and study interference within multiple virtual machines while trying to achieve load balancing and improve performance efficiency. Our goal is three-pronged. Firstly, we aim to understand the underlying support available for virtual machine migration and pursue new technologies or abstractions to improve efficiency and speed of the data transfer. Secondly, we carefully evaluate all the resources used by VMs for proper functioning and study the synchronization and multiplexing processes underneath which delineate when and where to migrate a virtual machine. Finally, we attempt to deduce the action to perform on running VMs (manipulation or resource configuration) so as to resolve the issue at hand. To achieve these goals, we follow a step-by-step procedure limiting the number of variable parameters and analyze the outcome of focal experiments. The results show that, using RDMA (Remote Direct Memory Access) to perform virtual machine migration can be used only in scenarios where the underlying hardware offers support for such transactions (eg. InfiniBand architecture) and such an abstraction over TCP/IP does not ameliorate efficiency of VM transfers. Further, a utility based function designed to analyze environment and application metrics and project an area of good/bad states on a map would require a plethora of parameters increasing its complexity. Considering VM re-distribution, one can predict the ideal number and time of migration of guest virtual machines on any configuration by gathering statistics from parallel migration for graphical analysis. Parallel VM migration gives us shorter average transfer time and higher latencies per VM. Pinning of virtual CPUs to VMs improves the performance efficiency of applications compared to sharing of CPUs.M.S.Includes bibliographical referencesby Siddharth Wag
Special Session: STT-MRAMs: Technology, Design and Test
Full text linkSTT-MRAM has long been a promising non-volatile memory solution for the embedded application space owing to its attractive characteristics such as non-volatility, low leakage, high endurance, and scalability. However, the operating requirements for high-performance computing (HPC) and low power (LP) applications involve different challenges. This paper addresses different aspects of STT-MRAM; it will cover state-of-the-art, some new results and future challenges related to technology, design and test. While STT-MRAM devices have shown encouraging performance metrics at device-level, a key challenge has been achieving backend-of-line (BEOL) CMOS compatibility, while retaining the benefits of low power operation. Scaling demands to improve data densities have placed additional challenges in terms of addressing the impact of process-induced damage on device performance at CD < 100 nm. In addition, the paper discusses the design of reliable read mechanism considering the variability effects. Moreover, the failure of traditional fault modeling and test approaches in model STT-MRAM unique defects for appropriate test solutions is demonstrated in this paper based on silicon data. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer EngineeringQuantum & Computer Engineerin
Computer vision methods for large-scale online clustering and quantitative dermatology
No full textIn modern computer vision applications where datasets are large and updates with new data may be ongoing, methods of online clustering are extremely important. Online clustering algorithms incrementally cluster the data points, use a fraction of the dataset memory, and update the clustering decisions when new data comes in. In this thesis we adapt a classic online clustering algorithm called Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) to incrementally cluster large datasets of features commonly used in computer vision, e.g., 840K color SIFT descriptors, 1.09 million color patches, 60K outlier corrupted grayscale patches, and 700K grayscale SIFT descriptors. We use the algorithm to cluster datasets consisting of non-convex clusters, e.g., Hopkins 155 3-D motion segmentation dataset. We call the adapted version modified-BIRCH (m-BIRCH). BIRCH was originally developed by the database management community. Modifications made in m-BIRCH enable data driven parameter selection and effectively handle varying density regions in the feature space. Data driven parameter selection automatically controls the level of coarseness of the data summarization. Effective handling of varying density regions is necessary to well represent the different density regions in the data summarization. Our implementation of the algorithm provides a useful clustering tool and is made publicly available. In the second part of the thesis, we present a micro-level feature based approach to register time-lapse skin images acquired over an extended period of time and multimodal skin images acquired in quick succession. Misregistration between the images makes it difficult to perform quantitative analysis and track the progression of skin disease. We demonstrate the utility of both types of registration, by employing the results for two quantitative dermatology tasks: 1) automatic detection of acne-like regions, and 2) separation of surface and subsurface reflection. Additionally, we have created a time-lapse video showing the registered time-lapse images, which clearly brings out the evolution of acne lesions with time.Ph. D.Includes bibliographical referencesby Siddharth K. Mada
MFA-MTJ Model: Magnetic-Field-Aware Compact Model of pMTJ for Robust STT-MRAM Design
Full text linkThe popularity of perpendicular magnetic tunnel junction (pMTJ)-based spin-transfer torque magnetic random access memories (STT-MRAMs) is growing very fast. The performance of such memories is very sensitive to magnetic fields, including both internal and external ones. This article presents a magnetic-field-aware compact model of pMTJ, named the MFA-magnetic tunnel junction (MTJ) model, for magnetic/electrical co-simulation of MTJ/CMOS circuits. Magnetic measurement data of MTJ devices, with diameters ranging from 35 to 175 nm, are used to calibrate an in-house magnetic coupling model. This model is subsequently integrated into our developed compact pMTJ model, which is implemented in Verilog-A. The superiority of the proposed MFA-MTJ model for device/circuit co-design of STT-MRAM is demonstrated by simulating a single pMTJ as well as STT-MRAM full circuits. The design space is explored under PVT variations and various configurations of magnetic fields.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Computer EngineeringQuantum & Computer Engineerin
Budhan Stories S2E8: We wanted to go back
No full textEpisode 8 of Season 2 - In this episode we spoke with migrant workers who walked thousands of kilometers to reach their homes in different part of India. We also spoke with labourers who travelled by train and faced hardships. When there was no livelihood options open due to lockdown, millions of labourers walked to their homes. This episode is an effort to capture the gist of their huge suffering on the way and in their hometowns. Created (Author) by: Siddharth Garange. Participants: Budhan Theatre, Dakxin Chhara, Atish Indrekar, Ruchika Kodekar, Chetna Rathod, Kushal Batunge, Keyur Bajrange, Anish Garange, Siddharth Garange, Alice Tilche, Akshay Khanna, Yashodara Udupa, Labour exodus, Labour on foot, Harsh Shodhan, Machan Theatre company, Nasim Akhtar.Supplementary materials include short clips, poster and subtitles.</p
Author Correction: Deficiency of TET3 leads to a genome-wide DNA hypermethylation episignature in human whole blood (npj Genomic Medicine, (2021), 6, 1, (92), 10.1038/s41525-021-00256-y):Deficiency of TET3 leads to a genome-wide DNA hypermethylation episignature in human whole blood (npj Genomic Medicine, (2021), 6, 1, (92), 10.1038/s41525-021-00256-y)
Full text linkIn this article the author name Siddharth Banka was incorrectly written as Sidharth Banka. The original article has been corrected.</p
Applications of U-net to diffuse optical tomography data: Image reconstruction and superresolution
Full text linkDi use optical tomography (DOT) is being investigated for effective functional brain imaging. It serves as a cheaper, less bulky and safer alternative to fMRI imaging which is the current gold standard. However, due to the complicated nature of the system, analytical reconstruction approaches begin to fail. Currently functional signal activations are reconstructed using an analytical approach but the quality of the image, especially in terms of resolution, is low. This is one of the primary barriers to making DOT the gold standard for functional brain imaging. This thesis presents the theory behind the inverse problem and discusses possible solutions to image reconstruction and superresolution problems in the DOT brain imaging space. With the growth in deep learning and its applications in medical imaging, a U-net based architecture is proposed to learn the mapping and estimate a higher resolution image. This work shows that the proposed deep learning model trained on simulated images from real-world fMRI images of the human brain can reconstruct higher resolution images while reducing the number of hallucinations.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01The student, Siddharth Muralidaran, accepted the attached license on 2021-04-28 at 13:55.The student, Siddharth Muralidaran, submitted this Thesis for approval on 2021-04-28 at 14:03.This Thesis was approved for publication on 2021-04-28 at 14:12.DSpace SAF Submission Ingestion Package generated from Vireo submission #16605 on 2021-09-16 at 17:06:40Made available in DSpace on 2021-09-17T02:34:50Z (GMT). No. of bitstreams: 2
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Rapid time-resolved brain imaging with multiple clinical contrasts using wave-shuffling
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 41-42).Magnetic Resonance Imaging (MRI) is a non-invasive but slow biomedical imaging modality that produces images of multiple desirable contrasts. Currently, multiple receive channels are used in MRI to exploit redundancy and sample fewer data points for faster acquisition. Wave-CAIPI is a recently introduced technique that modifies the acquisition scheme in a manner that improves the effectiveness of the receive channels, allowing for higher rates of acceleration. In this work, the principle behind Wave-CAIPI is applied to a compressed-sensing technique called Shuffling. In Shuffling, the spatial and temporal axis are randomly sampled and the data is reconstructed with temporal subspace constraints, yielding a time-series of images with desirable contrast. Combining Wave-CAIPI and Shuffling achieves rapid, time-resolved imaging of brain at 1mm-isotropic spatial resolution in clinically feasible times.by Siddharth Iyer.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
Characterization, Modeling, and Test of Intermediate State Defects in STT-MRAMs
No full textThe manufacturing process of STT-MRAM requires unique steps to fabricate and integrate magnetic tunnel junction (MTJ) devices which are data-storing elements. Thus, understanding the defects in MTJs and their faulty behaviors are paramount for developing high-quality test solutions. This article applies the advanced device-aware test to intermediate (IM) state defects in MTJ devices based on silicon measurements and circuit simulations. An IM state manifests itself as an abnormal third resistive state, which differs from the two bi-stable states of MTJ. We performed silicon measurements on MTJ devices with diameter ranging from 60nm to 120nm; the results show that the occurrence probability of IM state strongly depends on the switching direction, device size, and bias voltage. We demonstrate that the conventional resistor-based fault modeling and test approach fails to appropriately model and test such a defect. Therefore, device-aware test is applied. We first physically model the defect and incorporate it into a Verilog-A MTJ compact model and calibrate it with silicon data. Thereafter, this model is used for a systematic fault analysis based on circuit simulations to obtain accurate and realistic faults in a pre-defined fault space. Our simulation results show that an IM state defect leads to intermittent write transition faults. Finally, we propose and implement a device-aware test solution to detect the IM state defect.Computer EngineeringQuantum & Computer Engineerin
Characterization, Modeling and Test of Synthetic Anti-Ferromagnet Flip Defect in STT-MRAMs
No full textUnderstanding the manufacturing defects in magnetic tunnel junctions (MTJs), which are the data-storing elements in STT-MRAMs, and their resultant faulty behaviors are crucial for developing high-quality test solutions. This paper introduces a new type of MTJ defect: synthetic anti-ferromagnet flip (SAFF) defect, wherein the magnetization in both the hard layer and reference layer of MTJ devices undergoes an unintended flip to the opposite direction. Both magnetic and electrical measurement data of SAFF defect in fabricated MTJ devices is presented; it shows that such a defect reverses the polarity of stray field at the free layer of MTJ, while it has no electrical impact on the single isolated device. The paper also demonstrates that using the conventional fault modeling and test approach fails to appropriately model and test such a defect. Therefore device-Aware fault modeling and test approach is used. It first physically models the defect and incorporate it into a Verilog-A MTJ compact model, which is afterwards calibrated with silicon data. The model is thereafter used for fault analysis and modeling within an STT-MRAM array; simulation results show that a SAFF defect may lead to an intermittent Passive Neighborhood Pattern Sensitive Fault (PNPSF1i) when all neighboring cells are in logic '1' state. Finally, test solutions for such fault are discussed.Computer EngineeringQuantum & Computer Engineerin
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