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2016
By 2020, there will be 50 to 100 billion devices connected to the Internet. Two domains of hot research to address these high demands of data processing are the Internet of Things (IoT) and Big Data. The demands of these new applications are increasing faster than the development of new hardware particularly because of the slowdown of Moore’s law. The main reason of the ineffectiveness of the processing speed is the memory wall or Von Neumann bottleneck which is comming from speed differences between the processor and the memory. Therefore, a new fast and power-efficient hardware architecture is needed to respond to those huge demands of data processing. In this thesis, we introduce novel high performance architectures for next generation computing using emerging nanotechnologies such as memristors. We have studied unconventional computing methods both in the digital and the analog domains. However, the main focus and contribution is in Spiking Neural Network (SNN) or neuromorphic a...
IEEE Transactions on Multi-Scale Computing Systems
Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures2012 IEEE International Symposium on Circuits and Systems
Nanodevice-based novel computing paradigms and the neuromorphic approach2012 •
2019 •
The advancements in the field of Artificial Intelligence (AI) and technology has led to an evolution of an intelligent era. Neural networks, having the computational power and learning ability similar to the brain is one of the key AI technologies. Neuromorphic computing system (NCS) consists of the synaptic device, neuronal circuit, and neuromorphic architecture. Memristor are a promising candidate for neuromorphic computing systems, but when it comes to neuromorphic computing, the conductance behavior of the synaptic memristor or neuronal memristor needs to be studied thoroughly in order to fathom the neuroscience or computer science. Furthermore, there is a need of more simulation work for utilizing the existing device properties and providing guidance to the development of future devices for different performance requirements. Hence, development of NCS needs more simulation work to make use of existing device properties. This work aims to provide an insight to build neuronal cir...
2013 •
2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
An Efficient Programming Framework for Memristor-based Neuromorphic Computing2021 •
Memristor-based crossbars are considered to be promising candidates to accelerate vector-matrix computation in deep neural networks. Before being applied for inference, mem-ristors in the crossbars should be programmed to conductances corresponding to the network weights after software training. Existing programming methods, however, adjust conductances of memristors individually with many programming-reading cycles. In this paper, we propose an efficient programming framework for memristor crossbars, where the programming process is partitioned into the predictive phase and the fine-tuning phase. In the predictive phase, multiple memristors are programmed simultaneously with a memristor programming model and IR-drop estimation. To deal with the programming inaccuracy resulting from process variations, noise and IR-drop and move conductances to target values, memristors are fine-tuned afterwards to reach a specified programming accuracy. Simulation results demonstrate that the propo...
Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)
MNSIM: Simulation Platform for Memristor-based Neuromorphic Computing System2016 •
2016 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)
Combining a volatile and nonvolatile memristor in artificial synapse to improve learning in Spiking Neural Networks2016 •
With the end of Moore's law in sight, we need new computing architectures to satisfy the increasing demands of big data processing. Neuromorphic architectures are good candidates to low energy computing for recognition and classification tasks. We propose an event-based spiking neural network architecture based on artificial synapses. We introduce a novel synapse box that is able to forget and remember by inspiration from biological synapses. Two different volatile and nonvolatile memristor devices are combined in the synapse box. To evaluate the effectiveness of our proposal, we use system-level simulation in our Neural Network Scalable Spiking Simulator (N2S3) using the MNIST handwritten digit recognition dataset. The first results show better performance of our novel synapse than the traditional nonvolatile artificial synapses.
Advanced Functional Materials
A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing2012 •
The Journal of Cell Biology
Role of P-Selectin Cytoplasmic Domain in Granular Targeting In Vivo and in Early Inflammatory Responses1998 •
2011 •
2007 •
Psychonomic Science
Proactive interference in a T maze brightness-discrimination task1968 •
2012 •
2017 •
Proceedings of the Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
Evaluation of Proportional Odds and Continuation Ratio Models for Smoker in Indonesia2020 •
İktisadi İdari Bilimler Fakültesi dergisi
Bankacilik Sektörünün Ekonomi̇k Büyüme Ve Sürdürülebi̇li̇r Ekonomi̇k Kalkinma Üzeri̇ne Etki̇si̇: Türki̇ye Ekonomi̇si̇ Üzeri̇ne Nedenselli̇k Testleri̇ (1981-2009)2013 •
2022 •
Pamukkale University Journal of Engineering Sciences
Assembly Line Balancing Problem with Stochastic Sequence-Dependent Setup Times2015 •
Acta Societatis Botanicorum Poloniae
<i>Ranunculus dahlgreniae</i> (section <i>Batrachium</i>, Ranunculaceae), a new species from Crete, Greece, with remarks on taxonomy and phylogenetic relations within the section2023 •
Uludağ Üniversitesi Tıp Fakültesi dergisi
Anjiyotensin II Reseptör Antagonisti Losartanın Hipertansif Hemodiyaliz Olgularında Ambulatuar Kan Basıncı Üzerine Etkisi2002 •
TURKISH JOURNAL OF CHEMISTRY
Comparative study on the effect of precursors on the morphology and electronic properties of CdS nanoparticles2021 •
2019 •
2010 •
2012 •