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 25 for “"In-Memory Computing"”.
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Deep in-memory computing
There is much interest in embedding data analytics into sensor-rich platforms such as wearables, biomedical devices, autonomous vehicles, robots, and Internet-of-Things to provide these with decision-making capabilities. Such platforms often need to implement machine learning (ML) algorithms under …
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Energy-efficient Resistive In-memory Computing Architectures
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
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In-Memory Computing Architecture for Deep Learning Acceleration
<p>The ever-increasing demands of deep learning applications, especially the more powerful but intensive unsupervised deep learning models, overwhelm computation capability, communication capability, and storage capability of the modern general-purpose CPUs and GPUs. To accommodate the memory and …
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Stochastic In-memory Computing Using Magnetic Tunnel Junctions
Current computing hardware based on von Neumann architecture and digital CMOS circuits face strong challenges to further scale up for big AI models and data-centric applications. However, while being actively studied, it is still not clear which alternative computing paradigm is the best solution …
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NON-VOLATILE IN-MEMORY COMPUTING WITH SKYRMIONS AND PHASE CHANGE MEMORIES
The non-volatile in-memory compute engine (NVIMCE), which saves on the latency and energy associated with data movement between memory and processing elements in the conventional von Neumann architectures, is a crucial design technique for enabling ultra-low power intelligent edge devices. Due to …
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The Future of Computing: An Energy-Efficient In-Memory Computing Architectures with Emerging VGSOT MRAM Technology
… architecture with a capacity of 1.57-Mb storage including in-memory compuitng capability, leveraging state-of-the-art gate voltage assisted spin-orbit torque (VGSOT) magnetic random-access memory (MRAM) technology. Beyond its role as a non-volatile storage solution, this architecture facilitates …
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Detecting genomic elements of extreme conservation in higher eukaryotes by integration of hash mapping and cache-oblivious in-memory computing
Genomics is one of the first life science disciplines to enter the era of Big Data, facing challenges in all three dimensions--volume, variety, and velocity. Yet, in spite of a plethora of sequencing data, we are still far from creating a complete encyclopedia of functional and structural elements …
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Geometrically Programmed Nano-Resistors for Ultra-Robust Artificial Neural Network Accelerator
Despite the transformative advance in artificial intelligence (AI), the AI processing hardware have not matched the speed and power-efficiency requirement, restricting the realization of the full potential of AI and requiring innovation in AI hardware. Data transmission bottleneck between memory …
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Magnetic domain wall devices : from physics to system level application
Spintronics promises intriguing device paradigms where electron spin is used as the information token instead of its charge counterpart. Spin transfer torque-magnetic random access memory (STT-MRAM) is considered one of the most mature nonvolatile memory technologies for next generation computers. …
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Moving Toward Intelligence: A Hybrid Neural Computing Architecture for Machine Intelligence Applications
Rapid advances in machine learning have made information analysis more efficient than ever before. However, to extract valuable information from trillion bytes of data for learning and decision-making, general-purpose computing systems or cloud infrastructures are often deployed to train a …
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Computing Big-Data Applications Near Flash
Current systems produce a large and growing amount of data, which is often referred to as Big Data. Providing valuable insights from this data requires new computing systems to store and process it efficiently. For a fast response time, Big Data typically relies on in-memory computing, which …
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EMERGENT PHENOMENA AND NOVEL DEVICES BASED ON SPIN-ORBIT COUPLING AT COMPLEX OXIDE INTERFACES
… functional and energy-efficient devices utilizing novel quantum materials and properties. Spin-orbit coupling (SOC) has been pivotal to this effort as it offers an effective means to drive and manipulate magnetic properties – such as anisotropy, spin relaxation, magnetic damping, anisotropic …
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Analog-to-Digital Converters for Secure and Emerging AIoT Applications
… hardware. Analog neural networks (ANNs) with in-memory computing (IMC) using resistive random-access memory (RRAM) are promising architectures to reduce latency and increase energy efficiency for IoT devices. However, interface circuitry, including analog-to-digital converters (ADCs) between …
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Analog On-chip Training and Inference with Non-volatile Memory Devices
As the demand for computation in neural networks continues to rise, conventional computing resources are increasingly constrained by their limited energy efficiency. One promising solution to this challenge is analog in-memory computing (AIMC), which enables efficient matrix-vector multiplications …
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Spiking Neural Network with Memristive Based Computing-In-Memory Circuits and Architecture
In recent years neuromorphic computing systems have achieved a lot of success due to its ability to process data much faster and using much less power compared to traditional Von Neumann computing architectures. There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural …
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Nonlinear ion transport at high electric currents in shock electrodialysis and ion-intercalation memories
This thesis studies the nonlinear ion transport at high electric currents, in two applications: shock electrodialysis (shock ED) for ion separation, and ion-intercalation memories for in-memory computing. The two studies are both related to the concept of concentration polarization (CP) in …
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Finite precision deep learning with theoretical guarantees
Recent successes of deep learning have been achieved at the expense of a very high computational and parameter complexity. Today, deployment of both inference and training of deep neural networks (DNNs) is predominantly in the cloud. A recent alternative trend is to deploy DNNs onto untethered, …
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Nanoscale device engineering and plasmon-enhanced light – matter interactions for the characterization of 2D materials
The PhD thesis introduces nanoscale tools for optoelectronic characterization of 2D materials, addressing limitations of existing techniques including destructiveness, imprecision, and vacuum requirements. Three novel characterization methods are proposed. They all employ gold nanoparticles as …
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Magnetic tunnel junction devices and circuits for in-memory, neuromorphic and radiation hard computing
The magnetic tunnel junction is a memory device at the core of emerging magnetic random access memory technology. As CMOS technology is approaching its physical limits, spintronics, with benefits like non-volatility and normally-off behavior, is a promising candidate for next-generation artificial …
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Machine Learning for Analog/Mixed-Signal Integrated Circuit Design Automation
<p>Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart …
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