275 research outputs found
Annual Report 2006-2007
Contents:
Introduction......p. 1 Librarians Talk About the Library Collections......p. 2 Notable Acquisitions......p. 10 Events and Exhibits......p. 17 Personnel and Professional Activities......p. 20 Facts and Figures......p. 26 Buildings......p. 28 Library Development Activities......p. 29 University Libraries Support Groups......p. 29 Committees......p. 3
Energy-efficient hardware design based on high-level synthesis
This dissertation describes research activities broadly concerning the area of High-level synthesis (HLS), but more specifically, regarding the HLS-based design of energy-efficient hardware (HW) accelerators. HW accelerators, mostly implemented on FPGAs, are integral to the heterogeneous architectures employed in modern high performance computing (HPC) systems due to their ability to speed up the execution while dramatically reducing the energy consumption of computationally challenging portions of complex applications. Hence, the first activity was regarding an HLS-based approach to directly execute an OpenCL code on an FPGA instead of its traditional GPU-based counterpart. Modern FPGAs offer considerable computational capabilities while consuming significantly smaller power as compared to high-end GPUs. Several different implementations of the K-Nearest Neighbor algorithm were considered on both FPGA- and GPU-based platforms and their performance was compared. FPGAs were generally more energy-efficient than the GPUs in all the test cases. Eventually, we were also able to get a faster (in terms of execution time) FPGA implementation by using an FPGA-specific OpenCL coding style and utilizing suitable HLS directives.
The second activity was targeted towards the development of a methodology complementing HLS to automatically derive power optimization directives (also known as "power intent") from a system-level design description and use it to drive the design steps after HLS, by producing a directive file written using the common power format (CPF) to achieve power shut-off (PSO) in case of an ASIC design. The proposed LP-HLS methodology reduces the design effort by enabling designers to infer low power information from the system-level description of a design rather than at the RTL. This methodology required a SystemC description of a generic power management module to describe the design context of a HW module also modeled in SystemC, along with the development of a tool to automatically produce the CPF file to accomplish PSO. Several test cases were considered to validate the proposed methodology and the results demonstrated its ability to correctly extract the low power information and apply it to achieve power optimization in the backend flow
Graduate Catalog, 2011-2012
https://scholar.valpo.edu/gradcatalogs/1038/thumbnail.jp
Graduate Catalog, 2014-2015
https://scholar.valpo.edu/gradcatalogs/1041/thumbnail.jp
Graduate Catalog, 2012-2013
https://scholar.valpo.edu/gradcatalogs/1039/thumbnail.jp
Graduate Catalog, 2013-2014
https://scholar.valpo.edu/gradcatalogs/1040/thumbnail.jp
Clinical Decision Support System for Unani Medicine Practitioners
Like other fields of Traditional Medicines, Unani Medicines have been found
as an effective medical practice for ages. It is still widely used in the
subcontinent, particularly in Pakistan and India. However, Unani Medicines
Practitioners are lacking modern IT applications in their everyday clinical
practices. An Online Clinical Decision Support System may address this
challenge to assist apprentice Unani Medicines practitioners in their
diagnostic processes. The proposed system provides a web-based interface to
enter the patient's symptoms, which are then automatically analyzed by our
system to generate a list of probable diseases. The system allows practitioners
to choose the most likely disease and inform patients about the associated
treatment options remotely. The system consists of three modules: an Online
Clinical Decision Support System, an Artificial Intelligence Inference Engine,
and a comprehensive Unani Medicines Database. The system employs advanced AI
techniques such as Decision Trees, Deep Learning, and Natural Language
Processing. For system development, the project team used a technology stack
that includes React, FastAPI, and MySQL. Data and functionality of the
application is exposed using APIs for integration and extension with similar
domain applications. The novelty of the project is that it addresses the
challenge of diagnosing diseases accurately and efficiently in the context of
Unani Medicines principles. By leveraging the power of technology, the proposed
Clinical Decision Support System has the potential to ease access to healthcare
services and information, reduce cost, boost practitioner and patient
satisfaction, improve speed and accuracy of the diagnostic process, and provide
effective treatments remotely. The application will be useful for Unani
Medicines Practitioners, Patients, Government Drug Regulators, Software
Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of
Artificial Intelligence in Clinical Decision Support System for Unani
Medicine
Graduate Catalog, 2015-2016
https://scholar.valpo.edu/gradcatalogs/1042/thumbnail.jp
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