Routing brain traffic through the von Neumann bottleneck: Efficient cache usage in spiking neural network simulation code on general purpose computers

Simulation is a third pillar next to experiment and theory in the study of complex dynamic systems such as biological neural networks. Contemporary brain-scale networks correspond to directed graphs of a few million nodes, each with an in-degree and out-degree of several thousands of edges, where nodes and edges correspond to the fundamental biological units, neurons and synapses, respectively. When considering a random graph, each node's edges are distributed across thousands of parallel processes. The activity in neuronal networks is also sparse. Each neuron occasionally transmits a brief signal, called spike, via its outgoing synapses to the corresponding target neurons. This spatial and temporal sparsity represents an inherent bottleneck for simulations on conventional computers: Fundamentally irregular memory-access patterns cause poor cache utilization. Using an established neuronal network simulation code as a reference implementation, we investigate how common techniques to recover cache performance such as software-induced prefetching and software pipelining can benefit a real-world application. The algorithmic changes reduce simulation time by up to 50%. The study exemplifies that many-core systems assigned with an intrinsically parallel computational problem can overcome the von Neumann bottleneck of conventional computer architectures

A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

On the performance characterization and evaluation of RNA structure prediction algorithms for high performance systems

Ph.DDOCTOR OF PHILOSOPH

NASA Tech Briefs, December 1989

Topics include: Electronic Components and Circuits. Electronic Systems, Physical Sciences, Materials, Computer Programs, Mechanics, Machinery, Fabrication Technology, Mathematics and Information Sciences, and Life Sciences

Proceedings of the 1st International Conference on Live Coding

Open Access peer reviewed papers on live coding published at the 1st International Conference on Live Coding (ICLC) in Leeds

Collective Communications and Computation Mechanisms on the RF Channel for Organic Printed Smart Labels and Resource-limited IoT Nodes

Radio Frequency IDentification (RFID) and Wireless Sensor Networks (WSN) are seen as enabler technologies for realizing the Internet of Things (IoT). Organic and printed Electronics (OE) has the potential to provide low cost and all-printable smart RFID labels in high volumes. With regard to WSN, power harvesting techniques and resource-efficient communications are promising key technologies to create sustainable and for the environment friendly sensing devices. However, the implementation of OE smart labels is only allowing printable devices of ultra-low hardware complexity, that cannot employ standard RFID communications. And, the deployment of current WSN technology is far away from offering battery-free and low-cost sensing technology. To this end, the steady growth of IoT is increasing the demand for more network capacity and computational power. With respect to wireless communications research, the state-of-the-art employs superimposed radio transmission in form of physical layer network coding and computation over the MAC to increase information flow and computational power, but lacks on practicability and robustness so far. With regard to these research challenges we developed in particular two approaches, i.e., code-based Collective Communications for dense sensing environments, and time-based Collective Communications (CC) for resource-limited WSNs. In respect to the code-based CC approach we exploit the principle of superimposed radio transmission to acquire highly scalable and robust communications obtaining with it at the same time as well minimalistic smart RFID labels, that can be manufactured in high volume with present-day OE. The implementation of our code-based CC relies on collaborative and simultaneous transmission of randomly drawn burst sequences encoding the data. Based on the framework of hyper-dimensional computing, statistical laws and the superposition principle of radio waves we obtained the communication of so called ensemble information, meaning the concurrent bulk reading of sensed values, ranges, quality rating, identifiers (IDs), and so on. With 21 transducers on a small-scale reader platform we tested the performance of our approach successfully proving the scalability and reliability. To this end, we implemented our code-based CC mechanism into an all-printable passive RFID label down to the logic gate level, indicating a circuit complexity of about 500 transistors. In respect to time-based CC approach we utilize the superimposed radio transmission to obtain resource-limited WSNs, that can be deployed in wide areas for establishing, e.g., smart environments. In our application scenario for resource-limited WSN, we utilize the superimposed radio transmission to calculate functions of interest, i.e., to accomplish data processing directly on the radio channel. To prove our concept in a case study, we created a WSN with 15 simple nodes measuring the environmental mean temperature. Based on our analysis about the wireless computation error we were able to minimize the stochastic error arbitrarily, and to remove the systematic error completely

Open Pedagogy Approaches: Faculty, Library, and Student Collaborations

Open Pedagogy Approaches: Faculty, Library, and Student Collaborations is a collection of case studies from higher education institutions across the United States. An open educational resource (OER) in its own right, it offers a diverse compilation of OER and open pedagogy projects grounded in faculty, library, and student collaborations. Open Pedagogy Approaches provides ideas, practical tips, and inspiration for educators willing to explore the power of open, whether that involves a small innovation or a large-scale initiative. Particularly during this pandemic, as libraries struggle against publisher limitations to offer traditional print texts in e-format, libraries are a natural partner in the creation and facilitation of open educational resources and practices. “Going open” offers innovative alternatives that can equitably shift the culture of student access and empowerment in learning. List of chapters: Editor\u27s Preface / Alexis Clifton Foreword / Robin DeRosa Introduction / Kimberly Davies Hoffman, Robert Berkman, Deborah Rossen-Knill, Kristen Totleben, Eileen Daly-Boas, Alexis Clifton, Moriana Garcia, Lev Earle, and Joe Easterly Evolving into the Open: A Framework for Collaborative Design of Renewable Assignments / Stacy Katz and Jennifer Van Allen Informed Open Pedagogy and Information Literacy Instruction in Student-Authored Open Projects / Cynthia Mari Orozco Approaching Open Pedagogy in Community and Collaboration / Caroline Sinkinson and Amanda McAndrew Open Pedagogy Big and Small: Comparing Open Pedagogy Efforts in Large and Small Higher Education Settings / Shanna Hollich and Jacob Moore Adapting Open Educational Course Materials in Undergraduate General Psychology: A Faculty-Librarian-Student Partnership / Dennis E. Schell, Dorinne E. Banks, and Neringa Liutkaite Reading British Modernist Texts: A Case in Open Pedagogy / Mantra Roy, Joe Easterly, and Bette London Humanities in the Open: The Challenges of Creating an Open Literature Anthology / Christian Beck, Lily J. Dubach, Sarah A. Norris, and John Venecek A 2-for-1 Deal: Earn Your AA While Learning About Information Literacy Using OER / Mary Lee Cunill, Sheri Brown, and Tia Esposito Mathematics Courses and the Ohio Open Ed Collaborative: Collaborative Course Content Building for Statewide Use / Daniel Dotson, Anna Davis, Amanda L. Folk, Shanna Jaggars, Marcos D. Rivera, and Kaity Prieto Library Support for Scaffolding OER-enabled Pedagogy in a General Education Science Course / Lindsey Gumb and Heather Miceli Sharing the End of the World: Students’ Perceptions of Their Self-Efficacy in the Creation of Open Access Digital Learning Objects / Sarah Hutton, Lisa Di Valentino, and Paul Musgrave Teaching Wikipedia: A Model for Critical Engagement with Open Information / Amanda Koziura, Jennifer M. Starkey, and Einav Rabinovitch-Fox “And Still We Rise”: Open Pedagogy and Black History at a Rural Comprehensive State College / Joshua F. Beatty, Timothy C. Hartnett, Debra Kimok, and John McMahon Building a Collection of Openly Licensed Student-Developed Videos / Ashley Shea Whose History?: Expanding Place-Based Initiatives Through Open Collaboration / Sean D. Visintainer, Stephanie Anckle, and Kristen Weischedel Scholarly Bridges: SciComm Skill-Building with Student-Created Open Educational Resources / Carrie Baldwin-SoRelle and Jennifer M. Swann Harnessing the Power of Student-Created Content: Faculty and Librarians Collaborating in the Open Educational Environment / Bryan James McGeary, Ashwini Ganeshan, and Christopher S. Guder Open Pedagogical Practices to Train Undergraduates in the Research Process: A Case Study in Course Design and Co-Teaching Strategies / Stephanie N. Lewis, Anne M. Brown, and Amanda B. MacDonald Open Pedagogical Design for Graduate Student Internships, A New Collaborative Model / Laurie N. Taylor and Brian Keith Adventures in a Connectivist MOOC on Open Learning / Susan J. Erickson Invitation to Innovation: Transforming the Argument-Based Research Paper to Multimodal Project / Denise G. Malloy and Sarah Siddiqui “What If We Were To Go?”: Undergraduates Simulate the Building of an NGO From Theory To Practice / Kimberly Davies Hoffman, Rose-Marie Chierici, and Amanda Spencehttps://knightscholar.geneseo.edu/geneseo-authors/1010/thumbnail.jp

Intelligent decision support systems for optimised diabetes

Computers now pervade the field of medicine extensively; one recent innovation is the development of intelligent decision support systems for inexperienced or non-specialist pbysicians, or in some cases for use by patients. In this thesis a critical review of computer systems in medicine, with special reference to decision support systems, is followed by a detailed description of the development and evaluation of two new, interacting, intelligent decision support systems in the domain of diabetes. Since the discovery of insulin in 1922, insulin replacement therapy for the treatment of diabetes mellitus bas evolved into a complex process; there are many different formulations of insulin and much more information about the factors which affect patient management (e.g. diet, exercise and progression of complications) are recognised. Physicians have to decide on the most appropriate anti-diabetic therapy to prescribe to their patients. Insulin-treated patients also have to monitor their blood glucose and decide how much insulin to inject and when to inject it. In order to help patients determine the most appropriate dose of insulin to take, a simple-to-use, hand-held decision support system has been developed. Algorithms for insulin adjustment have been elicited and combined with general rules of therapy to offer advice for every dose. The utility of the system has been evaluated by clinical trials and simulation studies. In order to aid physician management, a clinic-based decision support system has also been developed. The system provides wide-ranging advice on all aspects of diabetes care and advises an appropriate therapy regimen according to individual patient circumstances. Decisions advised by the pbysician-related system have been evaluated by a panel of expert physicians and the system has undergone informal primary evaluation within the clinic setting. An interesting aspect of both systems is their ability to provide advice even in cases where information is lacking or uncertain