Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 13 of 13 for “"stochastic computing"”.
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System simulation using digital stochastic computing structures.
… study of the potential applications of digital stochastic computers. In particular, this work has considered the simulation of stochastic networks using digital computer software written in FORTRAN. The study of these networks was aided by hybrid computer simulations which were used to check on …
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Stochastic computation for energy-efficient robust ultra-low-power platforms
Next-generation ubiquitous computing promises new levels in immersion and seamless technology integration enabled through a profusion of embedded signal processing (DSP)-heavy ultra-low-power (ULP) platforms. This dissertation proposes an holistic integrated stochastic computing approach to enable …
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Study of vdW Magnetic Materials for Spintronic Applications
… is to replace the traditional von-Neumann computing hardware with technologies like neuromorphic and stochastic computing which are better suited for AI applications. Here, I study van der Waals magnetic materials for their application in developing spintronic devices to form the building …
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Stochastic Computation from Serial to Parallel
Stochastic Computing is a digital computation approach that operates on random bit streams to perform complex tasks with much smaller hardware footprints compared to conventional binary radix approaches. SC works based on the assumption that input bit streams are independent random sequences of …
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Programmable stochastic processors
… substantial promise for application-specific stochastic computing, i.e., computing that exploits application error tolerance to enable careful relaxation of correctness guarantees provided by hardware in order to reduce power. This dissertation explores the feasibility, challenges, and …
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Efficient machine learning: models and accelerations
… address these two problems and utilize different computing paradigms to solve real-life deep learning problems.</p> <p>To explore in these two domains, this thesis first presents the cogent confabulation network for sentence completion problem. We use Chinese language as a case study to describe …
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Stochastic architectures for probabilistic computation
… unknown. Here I show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. By connecting stochastic digital components according to simple mathematical rules, it is possible to rapidly, reliably …
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Computing with Spintronics: Circuits and architectures
… following contributions towards the design of computing platforms with spintronic devices. 1) It explores the use of spintronic memories in the design of a domain-specific processor for an emerging class of data-intensive applications, namely recognition, mining and synthesis (RMS). Two …
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Synthesis of stochastic learning automata.
… interest has developed in the field of stochastic learning automata theory and, consequently, the application areas for learning systems. In control engineering, they are viewed as a means to implement optimal adaptive controllers for situations where little or no a priori information on …
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Design Methods for Reliable Quantum Circuits
Quantum computing is an emerging technology that has the potential to change the perspectives and applications of computing in general. A wide range of applications are enabled: from faster algorithmic solutions of classically still difficult problems to theoretically more secure communication …
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Inference and Learning in Spiking Neural Networks for Neuromorphic Systems
<p>Neuromorphic computing is a computing field that takes inspiration from the biological and physical characteristics of the neocortex system to motivate a new paradigm of highly parallel and distributed computing to take on the demands of the ever-increasing scale and computational complexity of …
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Inference And Learning In Spiking Neural Networks For Neuromorphic Systems
<p>Neuromorphic computing is a computing field that takes inspiration from the biological and physical characteristics of the neocortex system to motivate a new paradigm of highly parallel and distributed computing to take on the demands of the ever-increasing scale and computational complexity of …
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Novel approaches for reliable and efficient circuit design
"In this research work, a suite of approaches are presented to improve reliability of 3D heterogeneous processors (3DHP) and to reduce the area overhead of asynchronous designs. This work is primarily divided into two parts. In the first part, we present an approach for improving reliability in …