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 18 of 18 for “"dot product"”.
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A dot product kernel using rapidly switched analog circuit
… to implement an energy-efficient mixed-signal dot product (DP) kernel for machine learning and signal processing applications. RSAC operates by fast switching the analog inputs to the output via variable width digital pulses. A description of the different components of RSAC, along with a …
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Topological Operations for Genus Distributions and Embeddings of Graphs
… treat one graph. We then consider the Cartesian product, dot product and extended dot product which are designed to be applied between two graphs. Face-contraction, vertex-splitting, vertex-augment, pearl-making, bouquet-making and face-expansion are discussed to achieve partial genus …
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Shear-free perfect fluid theorems in general relativity
… the vorticity are basic and the case where the dot product of the rescaled acceleration vector field and the unit vorticity vector is basic, leading to the existence of a Killing vector along the vorticity
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Algebraic Grid Generation Using Tensor Product B-Splines
… mapping at each interior grid point and the dot product of vectors tangent to the grid lines is investigated.</p> <p>Grids generated by using the algorithm are presented.</p>
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Efficient CNNs and Energy Efficient SRAM Design for ubiquitous medical devices
… random access memory (SRAM) with in-memory dot-product computation for low-power segmentation network implementation. The aim is to develop platform technology embodiments deployable across a wide range of health-monitoring wearable device applications requiring accurate, real-time and …
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Sparse Expansion and Neuronal Disentanglement
… of simpler ones, each with a separate sparse dot product covering it. Interestingly, we show that the Wasserstein distance between a neuron’s output distribution and a Gaussian distribution is an indicator of its entanglement level and contribution to the accuracy of the model. Every layer of …
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From GNNs to sparse transformers: graph-based architectures for multi-hop question answering
… GNNs, and compare the Transformer's Scaled Dot Product (SDP) attention to the Graph Attention Network [5] (GAT)'s Additive Attention [2]. We simplify existing GNNbased MHQA models and leverage this system to compare GNN architectures in a lower compute setting than token-level models. We …
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Stress relaxation of AZ31 alloy using high energy diffraction microscopy
… distinct plane families decreased linearly with dot product of loading-direction vector and corresponding basal plane normal vector. The thesis concludes the algorithm for analysis: image rotation, image combination, Gaussian filtering, peak isolation, trajectory generation, reciprocal …
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The Future of Computing: An Energy-Efficient In-Memory Computing Architectures with Emerging VGSOT MRAM Technology
… of logic-inside-memory (LinM/LiM), in-memory-dot- product multiplication tailored for binary-neural-networks, and content-accessable memory (CAM). Our designed bit-cell proposed in this architecture occupies a compact area of 0.195 μm2 and exhibits remarkable performance metrics. It achieves …
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Association of lingual myoarchitecture with local mechanics during swallowing determined by magnetic resonance imaging
… strain rate tensor was quantified by the dot product between the two vectors. Using this technique, the sagittal muscle activity was observed over the course of the swallow. In the first 200 ms after gating, the verticalis and palatoglossus contract in order to form the bolus.
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A Study of Magnetic Helicity in Decaying and Forced 3D-MHD Turbulence
… is defined as the volume integral of the dot product of the magnetic field and the magnetic vector potential. It characterizes the linkage and twists of the magnetic field lines. The inverse cascade is believed to be one of the causes of large-scale magnetic structure formation in the …
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Finite precision deep learning with theoretical guarantees
… criterion (OCC) to minimize the precision of dot-product outputs. For implementations using in-memory computing, OCC lowers ADC precision requirements. We analyze fixed-point training and present a methodology for implementing quantized back-propagation with close-to-minimal per-tensor …
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Random Interval Graphs
… that such a graph is defined by letting $X_{1},\ldots X_{n},Y_{1},\ldots Y_{n}$ be $2n$ independent random variables, with uniform distribution on $[0,1]$. We then say that the $i$th of the $n$ vertices is the interval $[X_{i},Y_{i}]$ if $X_{i}<Y_{i}$ and the interval $[Y_{i},X_{i}]$ if …
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Energy-efficient smart embedded memory design for IoT and AI
… learning applications. A 16Kb SRAM with embedded dot-product computation capability, is designed for binary-weight neural networks. Highly parallel analog processing in- side the memory array, provided better energy-efficiency than conventional digital implementations. With our variation-tolerant …
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Area-constrained frequency estimation for focal plane arrays
… the D-DHT and the DFT when mapped to the direct dot-product (DP) architecture. The minimum precision values for the input, weight, and the accumulator is combined with the area estimates of a 1-bit full adder (FA) and a 1-bit register in a 65 nm CMOS process to obtain the area costs of the D-DHT …
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IMPLEMENTATION OF A NOVEL INTEGRATED DISTRIBUTED ARITHMETIC AND COMPLEX BINARY NUMBER SYSTEM IN FAST FOURIER TRANSFORM ALGORITHM
… is a technique that is used to compute the inner dot product between two vectors without the use of any dedicated multipliers. These dedicated multipliers are fast but they consume a large amount of hardware and are quite costly. The DA multiplier process is accomplished by shifting and adding …
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On the analysis of complex networks : fundamental limits, scalable algorithms, and applications
… networks. Here we introduce logistic Random Dot Product Graphs (RDPGs) as a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a scalable spectral method that solves the maximum likelihood inference …
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Subsampling based inference for network data
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01