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Visar resultat 1 - 5 av 9 avhandlingar som matchar ovanstående sökkriterier.

1. 1. 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

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. Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits

   Författare :Philip Tully; Anders Lansner; Matthias Hennig; Gordon Pipa; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Bayes rule; synaptic plasticity and memory modeling; intrinsic excitability; naïve Bayes classifier; spiking neural networks; Hebbian learning; neuromorphic engineering; reinforcement learning; temporal sequence learning; attractor network; Computer Science; Datalogi;

   Sammanfattning : Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. LÄS MER

5. 4. Theoretical simulations of environment-sensitive dynamical systems for advanced reservoir computing applications

   Författare :Vasileios Athanasiou; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; environment-sensitive memristor; organic electrochemical transistor; Sensing; constant phase element; neuromorphic computations; memristor networks; memristor;

   Sammanfattning : The possibility of building intelligent sensing substrates that both collect information about an environment and analyze it in real-time has been investigated theoretically. In a typical setup, a dynamical system is assumed to interact with the environment over time. The system operates as a reservoir computer acting as a reservoir of states. 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