Results 81 to 90 of about 28,339 (330)

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

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

Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics

Advanced Functional Materials, EarlyView.
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha, Yingshan Ma, Yong Nam An, Sung‐Un An, Hyeon Hak Jung, Suvi‐Tuuli Varjamo, Jiwon Yoo, Junho Min, Hanvit Kim, Faisal Ahmed, Sang Hoon Chae, Young Min Song, Weiwei Cai, Tawfique Hasan, Zhipei Sun, Dong‐Ho Kang, Hyeon‐Jin Shin, Yunyun Dai, Hoon Hahn Yoon   +18 more
wiley   +1 more source

2D materials-based crossbar array for neuromorphic computing hardware

Neuromorphic Computing and Engineering
The growing demand for artificial intelligence has faced challenges for traditional computing architectures. As a result, neuromorphic computing systems have emerged as possible candidates for next-generation computing systems.
Hyeon Ji Lee, Sungwoo Park, Juhui Kim, Min Hyuk Park, Jihyun Kim, Jung Ah Lim, Ho Won Jang   +6 more
doaj   +1 more source

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Advanced Intelligent Systems, 2020
The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed,
Mostafa Rahimi Azghadi, Ying-Chen Chen, Jason K. Eshraghian, Jia Chen, Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J. Kenyon, Burt Fowler, Jack C. Lee, Yao-Feng Chang   +10 more
doaj   +1 more source

Recent Progress in Transistor‐Based Optoelectronic Synapses: From Neuromorphic Computing to Artificial Sensory System

Advanced Intelligent Systems, 2021
Neuromorphic electronics draw attention as innovative approaches that facilitate hardware implementation of next‐generation artificial intelligent system including neuromorphic in‐memory computing, artificial sensory perception, and humanoid robotics ...
Sung Woon Cho, Sung Min Kwon, Yong-Hoon Kim, Sung Kyu Park   +3 more
doaj   +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

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

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +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