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Algorithm and Hardware Design for Efficient Deep Learning Inference

Abstract Deep learning (DL) has proved itself be one of the most important developements till date with far reaching impacts in numerous fields like robotics, computer vision, surveillance, speech processing, machine translation, finance, etc. They are now widely used for countless applications because of their ability to generalize real world data, robustness to noise in previously unseen data and high inference accuracy. With the ability to learn useful features from raw sensor data, deep learning algorithms have out-performed tradinal AI algorithms and pushed the boundaries of what can be achieved with AI. In this work, we demonstrate the power of deep learning by developing a neural network to automatically detect cough instances from audio reco... (more)
Created Date 2018
Contributor Mohanty, Abinash (Author) / Cao, Yu (Advisor) / Seo, Jae-sun (Committee member) / Vrudhula, Sarma (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / artificial intelligence / deep learning / hardware acceleration / machine learning / neural networks / resistive ram crossbars
Type Doctoral Dissertation
Extent 151 pages
Language English
Note Doctoral Dissertation Electrical Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis