Sökning: "Neuromorphic computing"

Visar resultat 1 - 5 av 18 avhandlingar innehållade orden Neuromorphic computing.

1. 1. Ag2S-Based Flexible Memristors for Neuromorphic Computing

   Författare :Yuan Zhu; Zhen Zhang; Qing Cao; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Ag2S film; flexible memristor; resistance switching; CMOS-compatible integration; neuromorphic computing;

   Sammanfattning : 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. LÄS MER

3. 2. Event-Driven Architectures for Heterogeneous Neuromorphic Computing Systems

   Författare :Mattias Nilsson; Fredrik Sandin; Foteini Liwicki; Jerker Delsing; Chiara Bartolozzi; Luleå tekniska universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Neuromorphic computing; Mixed-signal; Low-power; Non-von Neumann; Spatiotemporal pattern recognition; System integration; Cyber-Physical Systems; Cyberfysiska system;

   Sammanfattning : 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. LÄS MER

4. 3. Silicon Tracking and a Search for Long-lived Particles

   Författare :Rebecca Carney; Samuel Silverstein; Sara Strandberg; Maurice Garcia-Sciveres; Joel Goldstein; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; ATLAS; silicon; silicon tracking; radiation damage; neuromorphic; neuromorphic computing; long-lived particles; susy; rpvll; displaced vertices; pixel; pixel detector; fysik; Physics;

   Sammanfattning : 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. LÄS MER

5. 4. Flexible Electrical and Photoelectrical Artificial Synapses for Neuromorphic Systems

   Författare :Kunlong Yang; Fernando Seoane; Li-rong Zheng; Juha Plosila; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Flexible electronics; Neuromorphic network; Memristor; Electron trapping; Electrical artificial synapse; Photoelectrical artificial synapse; Applied Medical Technology; Tillämpad medicinsk teknik;

   Sammanfattning : 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. LÄS MER

7. 5. Ferroelectric Memristors - Materials, Interfaces and Applications

   Författare :Robin Athle; Institutionen för elektro- och informationsteknik; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; neuromorphic computing; Hafnium oxide; Memristor; Ferroelectric;

   Sammanfattning : 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. LÄS MER