Results 51 to 60 of about 14,261 (179)

Tunneling magnetoresistance materials and devices for neuromorphic computing

Materials Futures, 2023
Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands.
Yuxuan Yao, Houyi Cheng, Boyu Zhang, Jialiang Yin, Daoqian Zhu, Wenlong Cai, Sai Li, Weisheng Zhao   +7 more
doaj   +1 more source

Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines

, 2019
This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes
Chakrabartty, S., Chatterjee, O., Ghosh, A., Kumar, P., Nair, A. R., Paul, T., Thakur, C. S.   +6 more
core   +1 more source

A Low-Power High-Speed Spintronics-Based Neuromorphic Computing System Using Real-Time Tracking Method [PDF]

IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2018
In spintronic-based neuromorphic computing systems (NCSs), the switching of magnetic moment in a magnetic tunnel junction (MTJ) is used to mimic neuron firing. However, the stochastic switching behavior of the MTJ and process variations effect lead to a significant increase in the stimulation time of such NCSs.
Hooman Farkhani, Mohammad Tohidi, Sadaf Farkhani, Jens Kargaard Madsen, Farshad Moradi   +4 more
openaire   +1 more source

Van der Waals materials-based floating gate memory for neuromorphic computing

Chip, 2023
: With the advent of the “Big Data Era”, improving data storage density and computation speed has become more and more urgent due to the rapid growth in different types of data.
Qianyu Zhang, Zirui Zhang, Ce Li, Renjing Xu, Dongliang Yang, Linfeng Sun   +5 more
doaj   +1 more source

Stochastic Memristive Devices for Computing and Neuromorphic Applications

, 2013
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems.
Choi, Shinhyun, Gaba, Siddharth, Lu, Wei, Sheridan, Patrick, Zhou, Jiantao   +4 more
core   +1 more source

Highly Scalable Neuromorphic Hardware with 1-bit Stochastic nano-Synapses

, 2013
Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuations on switching probability of emerging magnetic switches are probabilistic phenomena in nature, and thus, processes of binary switching in these ...
Kavehei, Omid, Skafidas, Efstratios
core   +1 more source

Memcapacitive Devices in Logic and Crossbar Applications [PDF]

, 2017
Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits, reducing the energy
Teuscher, Christof, Tran, Dat
core   +2 more sources

Perspective: Organic electronic materials and devices for neuromorphic engineering

, 2018
Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies (ICT). In fact, new computing solutions and new hardware platforms are
Alibart, Fabien, Pecqueur, Sebastien, Vuillaume, Dominique   +2 more
core   +2 more sources

LiNbO3-based memristors for neuromorphic computing applications: a review

Frontiers in Electronic Materials
Neuromorphic computing is a promising paradigm for developing energy-efficient and high-performance artificial intelligence systems. The unique properties of lithium niobate-based (LiNbO3)-based memristors, such as low power consumption, non-volatility ...
Caxton Griffith Kibebe, Yue Liu
doaj   +1 more source

Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures

, 2017
Deep artificial neural networks (ANNs) can represent a wide range of complex functions. Implementing ANNs in Von Neumann computing systems, though, incurs a high energy cost due to the bottleneck created between CPU and memory.
Gupta, Jayesh K, Kochenderfer, Mykel, Mern, John   +2 more
core   +1 more source