Results 41 to 50 of about 2,883,478 (184)

Spintronic leaky-integrate-fire spiking neurons with self-reset and winner-takes-all for neuromorphic computing

Nature Communications, 2023
Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation of Moore’s law.
Di Wang, Ruifeng Tang, Huai Lin, Long Liu, Nuo Xu, Yan Sun, Xuefeng Zhao, Ziwei Wang, Dandan Wang, Z. Mai, Yongjian Zhou, Nan Gao, C. Song, Lijun Zhu, Tom Wu, Ming Liu, Guozhong Xing   +16 more
semanticscholar   +1 more source

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

, 2017
Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural ...
Aimone, James B., Carlson, Kristofor D., Donaldson, Jonathon, Follett, David R., Follett, Pamela L., Hill, Aaron J., James, Conrad D., Naegle, John H., Smith, Michael R., Vineyard, Craig M.   +9 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

Ultra-low power carbon nanotube/porphyrin synaptic arrays for persistent photoconductivity and neuromorphic computing

Nature Communications
Developing devices with a wide-temperature range persistent photoconductivity (PPC) and ultra-low power consumption remains a significant challenge for optical synaptic devices used in neuromorphic computing. By harnessing the PPC properties in materials,
Jian Yao, Qinan Wang, Yong Zhang, Yu Teng, Jing Li, Pin Zhao, Chun Zhao, Ziyi Hu, Zongjie Shen, Liwei Liu, Dan Tian, Song Qiu, Zhongrui Wang, Lixing Kang, Qingwen Li   +14 more
semanticscholar   +1 more source

A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation

, 2018
Memristive devices represent a promising technology for building neuromorphic electronic systems. In addition to their compactness and non-volatility features, they are characterized by computationally relevant physical properties, such as state ...
Indiveri, Giacomo, Muller, Lorenz K., Nair, Manu V, Payvand, Melika   +3 more
core   +1 more source

Ferroelectric artificial synapses for high-performance neuromorphic computing: Status, prospects, and challenges

Applied Physics Letters
Neuromorphic computing provides alternative hardware architectures with high computational efficiencies and low energy consumption by simulating the working principles of the brain with artificial neurons and synapses as building blocks.
Le Zhao, Hongdou Fang, Jie Wang, Fang Nie, Rongqi Li, Yuling Wang, Limei Zheng   +6 more
semanticscholar   +1 more source

Large-scale neuromorphic computing systems

Journal of Neural Engineering, 2016
Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the ...
openaire   +2 more sources

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

Improving classification accuracy of feedforward neural networks for spiking neuromorphic chips

, 2017
Deep Neural Networks (DNN) achieve human level performance in many image analytics tasks but DNNs are mostly deployed to GPU platforms that consume a considerable amount of power.
Mashford, Benjamin Scott, Tang, Jianbin, Yepes, Antonio Jimeno   +2 more
core   +1 more source

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

, 2017
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices.
Adiraju, Prathyusha, Catthoor, Francky, Das, Anup, Dutt, Nikil, Groenendaal, Willemijn, Krichmar, Jeffrey L., Pradhapan, Paruthi, Rajan, Raj Thilak, Schaafsma, Siebren, Van Hoof, Chris   +9 more
core   +1 more source