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 20 of 55 for “"Neuromorphic computing"”.
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Spike Processing Circuit Design for Neuromorphic Computing
… major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain increasing attention as compact and energy-efficient computing platforms. Spike based-neuromorphic computing systems require high performance and low power neural encoder and …
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Algorithm Hardware Codesign for High Performance Neuromorphic Computing
… taking inspiration from biological systems, neuromorphic computing and Spiking Neural Network (SNN) have drawn attention as alternative solutions for energy-efficient machine intelligence.</p><p>Though believed promising, neuromorphic computing are hardly used for real world applications. A …
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Neuromorphic computing systems : crystalline resistive random access memory
Neuromorphic computing is a promising approach for efficient electronics by shaping computer hardware like the human brain. At the core of neuromorphic architectures are artificial synapses, which store conductance states to weight collections of electrical spikes according to Kirchoff's laws and …
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Highly Efficient Neuromorphic Computing Systems With Emerging Nonvolatile Memories
<p>Emerging nonvolatile memory based hardware neuromorphic computing systems have enabled the implementation of general vector-matrix multiplication in a manner to fuse computation and memory at the same physical location. However, there remain three major challenges in designing such neuromorphic …
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Device-Level Modeling of Photonic Integrated Circuits for Neuromorphic Computing
L'abstract è presente nell'allegato / the abstract is in the attachment
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Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing
… proposed as an artificial synapse for creating neuromorphic computer applications. Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an …
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Machine learning inspired synthetic biology: neuromorphic computing in mammalian cells
Synthetic biologists seek to collect, refine, and repackage nature so that it's easier to design new and reliable biological systems, typically at the cellular or multicellular level. These redesigned systems are often referred to as "biological circuits," for their ability to perform operations on …
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Energy-efficient Neuromorphic Computing for Resource-constrained Internet of Things Devices
… deep learning techniques heavily depend on cloud computing for computation and storage. However, cloud computing faces technical issues with long latency, poor reliability, and weak privacy, resulting in the need for on-device computation and storage. Also, on-device computation is essential for …
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From Neuron Models to Edge Intelligence: A Multi-Level Exploration of Neuromorphic Computing
L'abstract è presente nell'allegato / the abstract is in the attachment
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ENGINEERING THE COMPLEX ELECTRICAL RESPONSE OF METALLIC CLUSTER-ASSEMBLED FILMS FOR NEUROMORPHIC COMPUTING APPLICATIONS
… network operations along with network topology. Neuromorphic Computing (NC) draws inspiration from the structure and operation of the brain to offer an alternative to the present Von Neumann paradigm of computation. Metallic cluster-assembled films (MCAFs) constitute a potential physical …
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Large-scale neuromorphic computing hardware for analog AI enabled by epitaxial random access memory
A neuromorphic computing on memristor-based crossbars is one of promising next generation analog computing methods since it features fast switching speed, extremely small cell footprint, low energy consumption for matrix-vector multiplication, capability of both storage and computing, …
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Design and FPGA Implementation of Optimized and Digital Multiplexing Spiking Encoders for Neuromorphic Computing
… their programmability and massively parallel computing capa bilities to enable high-performance hardware implementation. Lastly, the thesis provides a comprehensive analysis of the proposed spiking encoder and points out the future research direction of the spiking encoding algorithm. The …
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Enabling Energy-Efficient Hybrid CMOS and Embedded Memory Accelerators for Neuromorphic Computing at the Edge
… constraints. The first contribution explores computing-in-memory (CIM) architectures using memristors, which combine storage and computation to reduce data movement. While memristors offer density, low power, and nonvolatility, challenges such as resistance variation degrade inference …
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Protonic All-Solid-State Electrochemical Device as an Artificial Synapse for CMOS-Compatible Neuromorphic Computing
… information technology.[1] However, conventional computing hardware is energetically unfavorable to handle multifarious AI tasks because the frequent data transfer between the physically separated microprocessors and data storage units results in ‘memory wall bottleneck’, leading to high energy …
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Powering Next-Generation Artificial Intelligence by Designing Three-dimensional High-Performance Neuromorphic Computing System with Memristors
… to design a three-dimensional high-performance neuromorphic computing system. The low-variation memristors (fabricated by Virginia Tech) reduce the learning accuracy of the system significantly through adding heat dissipation layers. Moreover, three emerging neuromorphic architectures are …
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Development of Strontium Titanate Based Nanomaterials for Resistive Switching Applications
… for next generation non-volatile memory and neuromorphic computing applications. In general, resistive switching device consists of a two-terminal metal-insulator-metal structure, in which metal oxide is widely employed as the insulator. Among a variety of metal oxides, SrTiO3 has attracted …
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Design and Optimization of Temporal Encoders using Integrate-and-Fire and Leaky Integrate-and-Fire Neurons
… a new form of signal processing is needed. Neuromorphic computing has used inspiration from biology to produce a new form of signal processing by mimicking biological neural networks using electrical components. Neuromorphic computing requires less signal preprocessing than digital systems …
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Monte Carlo Simulations on Resistive Switching Memristor Modeling
… has been shown to be an important attribute in neuromorphic computing applications, and can aid experimentalists and manufacturers in refining memristor designs.
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An Energy-Efficient Spiking CNN Implementation for Cross-Patient Epileptic Seizure Detection
… their superior classification performance, a neuromorphic computing strategy for seizure prediction called spiking CNN is developed from the traditional CNN method, which is motivated by the energy-efficient spiking neural networks (SNNs) of the human brain.
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Ferroelectric doped hafnium oxide and its application on electronic devices
… proves the possibility of using our FTJ as a neuromorphic computing chip.
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