Search for dissertations about: "Neuromorphic computing"
Showing result 1 - 5 of 18 swedish dissertations containing the words Neuromorphic computing.
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1. Ag2S-Based Flexible Memristors for Neuromorphic Computing
Abstract : Memristive crossbar arrays hold the great promise for fast and energy efficient neuromorphic computing due to their parallel data storage and processing capabilities. As the key component, memristor should achieve stable resistance switching (RS) characteristics with low energy inputs and be compatible with complementary metal–oxide–semiconductor (CMOS) technology. READ MORE
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2. Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems
Abstract : Mixed-signal neuromorphic processors have brain-like organization and device physics optimized for emulation of spiking neural networks (SNNs), and offer an energy-efficient alternative for implementing artificial intelligence in applications where deep learning based on conventional digital computing is unfeasible or unsustainable. However, efficient use of such hardware requires appropriate configuration of its inhomogeneous, analog neurosynaptic circuits, with methods for sparse, spike-timing-based information encoding and processing. READ MORE
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3. Silicon Tracking and a Search for Long-lived Particles
Abstract : The ATLAS Detector, below the surface of the Swiss-French border, measures the remnants of high-energy proton-proton collisions, accelerated by the Large Hadron Collider (LHC) at CERN. Recently the LHC paused operations, having delivered an integrated luminosity corresponding to 150 fb−1 of data at a centre-of-mass energy of 13 TeV. READ MORE
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4. Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems
Abstract : Over the past decade, the field of personal electronic systems has trended toward mobile and wearable devices. However, the capabilities of existing electronic systems are overwhelmed by the computing demands at the wearable sensing stage. Two main bottlenecks are encountered. READ MORE
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5. Ferroelectric Memristors - Materials, Interfaces and Applications
Abstract : The backbone of modern computing systems rely on two key things: logic and memory, and while computing power hasseen tremendous advancements through scaling of the fundamental building block – the transistor, memory access hasn’tevolved as rapidly, leading to significant memory-bound systems. Additionally, the rapid evolution of machine learningand deep neural network (DNN) applications, has exposed the fundamental limitations of the traditional von Neumanncomputing architecture, due to its heavy reliance on memory access. READ MORE