Accelerating Time Series Analysis via Processing using Non-Volatile Memories

Time Series Analysis (TSA) is a critical workload for consumer-facing devices. Accelerating TSA is vital for many domains as it enables the extraction of valuable information and predict future events. The state-of-the-art algorithm in TSA is the subsequence Dynamic Time Warping (sDTW) algorithm. However, sDTW's computation complexity increases quadratically with the time series' length, resulting in two performance implications. First, the amount of data parallelism available is significantly higher than the small number of processing units enabled by commodity systems (e.g., CPUs). Second, sDTW is bottlenecked by memory because it 1) has low arithmetic intensity and 2) incurs a large memory footprint. To tackle these two challenges, we leverage Processing-using-Memory (PuM) by performing in-situ computation where data resides, using the memory cells. PuM provides a promising solution to alleviate data movement bottlenecks and exposes immense parallelism. In this work, we present MATSA, the first MRAM-based Accelerator for Time Series Analysis. The key idea is to exploit magneto-resistive memory crossbars to enable energy-efficient and fast time series computation in memory. MATSA provides the following key benefits: 1) it leverages high levels of parallelism in the memory substrate by exploiting column-wise arithmetic operations, and 2) it significantly reduces the data movement costs performing computation using the memory cells. We evaluate three versions of MATSA to match the requirements of different environments (e.g., embedded, desktop, or HPC computing) based on MRAM technology trends. We perform a design space exploration and demonstrate that our HPC version of MATSA can improve performance by 7.35x/6.15x/6.31x and energy efficiency by 11.29x/4.21x/2.65x over server CPU, GPU and PNM architectures, respectively

Improved Decentralized Fractional PD Control of Structure Vibrations

This paper presents a new strategy for the control of large displacements in structures under earthquake excitation. Firstly, an improved fractional order proportional-derivative (FOPD) controller is proposed. Secondly, a decentralized strategy is designed by adding a regulator and fault self-regulation to a standard decentralized controller. A new control architecture is obtained by combining the improved FOPD and the decentralized strategy. The parameters of the control system are tuned using an intelligent optimization algorithm. Simulation results demonstrate the performance and reliability of the proposed method.The work was supported by the National Natural Science Foundation of China (No. 11971032).info:eu-repo/semantics/publishedVersio

Extended and Generic Higher-Order Elements for MEMS Modeling

State-dependent resistors, capacitors, and inductors are a common part of many smart engineering solutions, e.g., in MEMS (Micro-Electro-Mechanical Systems) sensors and actuators, Micro/NanoMachines, or biomimetic systems. These memory elements are today modeled as generic and extended memristors (MR), memcapacitors (MC), and meminductors (ML), which are more general versions of classical MR, MC, and ML from the infinite set of the fundamental elements of electrical engineering, known as Higher-Order Elements (HOEs). It turns out that models of many complex phenomena in MEMS cannot be constructed only from classical and state-dependent elements such as R, L, and C, but that other HOEs with generalized behavior should also be used. Thus, in this paper, generic and extended versions of HOEs are introduced, overcoming the existing limitation to MR, MC, and ML elements. The relevant circuit theorems are formulated, which generalize the well-known theorems of classical memory elements, and their application to model complex processes of various physical natures in MEMS is shown

Memristors and Superconducting Quantum Interference Filters in RF Systems

<p>Complex nonlinear dynamical systems have been appeared in many fields of science and engineering. We are curious about two specific instances of those systems. Those two instances connect memristors and Josephson junctions to the electromagnetic fields. The first instance investigated microstrip patch antenna embedding dual memristors. This hybrid system produces broadband radiation in a narrow band radiation structure. The second one studies the novel ultra-sensitive magnetic field receiver implemented by superconducting quantum interference filters (SQIFs).</p><p>For the first instance, we notice that memristor has been proposed as the fourth passive element. We start with investigating the circuit model of this memristive element. Then, we embedded this circuit model into an EM radiation structure. We first report an efficient broadband electromagnetic radiation from a narrowband microstrip patch antenna. The directly modulated microstrip patch antenna system with dual memristors is calculated by using an integrated full-wave finite-difference time-domain solver and an embedded SPICE3 solver. Nonlinear transient electromagnetic responses are analyzed. The radiation frequency spectrum demonstrates the broadband radiation performance from the narrowband antenna system. We predict that the conceptual challenge of high frequency memristors will stimulate pioneering work in the fields of microwave and memristors.</p><p>For the second one, we predict that superconducting quantum interference filters (SQIFs) might play a key role in future quantum wireless communication systems. We analyze the dynamic behavior of this large-scale 2D DC SQIF (two-dimensional superconducting direct current quantum interference filter) array in a dynamic electromagnetic environment. The investigation under this framework starts from the SPICE circuit description of a RCSJ (resistively and capacitively shunted junction) model of a Josephson junction and then extends to the 2D SQIF with few device parameters. We separate the interface and the implementation of 2D DC SQIF. This approach can significantly improve circuit-level design efficiency of 2D SQIF array and ultimately allows us to accelerate the hybrid design with an electromagnetic radiation structure. Our findings on the average voltage response of this device offer compelling evidence that the bias static magnetic field plays a key role in designing an effective far-field magnetic field sensor. Since this device can function as both a robust and sensitive low noise pre-amplifier as well as a receiving antenna which only senses the magnetic field component of far-field electromagnetic wave signals, we call it magnetic-antenna or B-antenna. We believe that our research not only directly benefits the sensor design for Information Operations/Signals Intelligence (IO/SIGINT) applications in Very High Frequency/Ultra High Frequency (VHF/UHF) bands, but also opens new dimension of novel ultra-sensitive receiving antenna technology.</p>Dissertatio

Resistive Switching in Silicon-rich Silicon Oxide

Over the recent decade, many different concepts of new emerging memories have been proposed. Examples of such include ferroelectric random access memories (FeRAMs), phase-change RAMs (PRAMs), resistive RAMs (RRAMs), magnetic RAMs (MRAMs), nano-crystal floating-gate flash memories, among others. The ultimate goal for any of these memories is to overcome the limitations of dynamic random access memories (DRAM) and flash memories. Non-volatile memories exploiting resistive switching – resistive RAM (RRAM) devices – offer the possibility of low programming energy per bit, rapid switching, and very high levels of integration – potentially in 3D. Resistive switching in a silicon-based material offers a compelling alternative to existing metal oxide-based devices, both in terms of ease of fabrication, but also in enhanced device performance. In this thesis I demonstrate a redox-based resistive switch exploiting the formation of conductive filaments in a bulk silicon-rich silicon oxide. My devices exhibit multi-level switching and analogue modulation of resistance as well as standard two-level switching. I demonstrate different operational modes (bipolar and unipolar switching modes) that make it possible to dynamically adjust device properties, in particular two highly desirable properties: non-linearity and self-rectification. Scanning tunnelling microscopy (STM), atomic force microscopy (AFM), and conductive atomic force microscopy (C-AFM) measurements provide a more detailed insight into both the location and the dimensions of the conductive filaments. I discuss aspects of conduction and switching mechanisms and we propose a physical model of resistive switching. I demonstrate room temperature quantisation of conductance in silicon oxide resistive switches, implying ballistic transport of electrons through a quantum constriction, associated with an individual silicon filament in the SiOx bulk. I develop a stochastic method to simulate microscopic formation and rupture of conductive filaments inside an oxide matrix. I use the model to discuss switching properties – endurance and switching uniformity

Air Force Institute of Technology Research Report 2014

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

EFFICIENTLY ACCELERATING SPARSE PROBLEMS BY ENABLING STREAM ACCESSES TO MEMORY USING HARDWARE/SOFTWARE TECHNIQUES

The objective of this research is to improve the performance of sparse problems that have a wide range of applications but still, suffer from serious challenges when running on modern computers. In summary, the challenges include the underutilization of available memory bandwidth because of lack of spatial locality, dependencies in computation, or slow mechanisms for decompressing the sparse data, and the underutilization of concurrent compute engines because of the distribution of non-zero values in sparse data. Our key insight to address the aforementioned challenges is that based on the type of the problem, we either use an intelligent reduction tree near memory to process data while gathering them from random locations of memory, transform the computations mathematically to extract more parallelism, modify the distribution of non-zero elements, or change the representation of sparse data. By applying such techniques, the execution adapts more effectively to given hardware resources. To this end, this research introduces hardware/software techniques to enable stream accesses to memory for accelerating four main categories of sparse problems including the inference of recommendation systems, iterative solvers of partial differential equations (PDEs), deep neural networks (DNNs), and graph algorithms.Ph.D

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

11th International Conference on Business, Technology and Innovation 2022

Welcome to IC – UBT 2022 UBT Annual International Conference is the 11th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: Security Studies Sport, Health and Society Psychology Political Science Pharmaceutical and Natural Sciences Mechatronics, System Engineering and Robotics Medicine and Nursing Modern Music, Digital Production and Management Management, Business and Economics Language and Culture Law Journalism, Media and Communication Information Systems and Security Integrated Design Energy Efficiency Engineering Education and Development Dental Sciences Computer Science and Communication Engineering Civil Engineering, Infrastructure and Environment Architecture and Spatial Planning Agriculture, Food Science and Technology Art and Digital Media This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event. Edmond Hajrizi, President of UBT UBT – Higher Education Institutio