Results 61 to 70 of about 7,322 (252)

Dynamic event-based optical identification and communication

Frontiers in Neurorobotics
Optical identification is often done with spatial or temporal visual pattern recognition and localization. Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range, and accurate tracking.
Axel von Arnim, Jules Lecomte, Naima Elosegui Borras, Naima Elosegui Borras, Stanisław Woźniak, Angeliki Pantazi   +5 more
doaj   +1 more source

Principled neuromorphic reservoir computing [PDF]

Nature Communications
Abstract Reservoir computing advances the intriguing idea that a nonlinear recurrent neural circuit—the reservoir—can encode spatio-temporal input signals to enable efficient ways to perform tasks like classification or regression. However, recently the idea of a monolithic reservoir network that simultaneously buffers input signals and ...
Denis Kleyko, Christopher J. Kymn, Anthony Thomas, Bruno A. Olshausen, Friedrich T. Sommer, E. Paxon Frady   +5 more
openaire   +4 more sources

Meniscus Pixel Printing for Contact‐Lens Vision Sensing and Robotic Control

Advanced Functional Materials, EarlyView.
A visual‐sensing contact lens is enabled by meniscus pixel printing (MPP), which rapidly patterns a 200 µm perovskite photodetector pixel in 1 s without masks, vacuum processing, or bulky equipment. A deep‐learning‐based super‐resolution reconstructs sparse on‐lens signals into 80 × 80 high‐resolution visual information, while AI‐driven eye‐tracking ...
Byung‐Hoon Gong, Dohyean Kim, Jiyun Jeong, Taekyeong Kim, Soon‐sung Kwon, Junyoung Seo, Il Jeon, Im Doo Jung   +7 more
wiley   +1 more source

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

Emerging Materials for Neuromorphic Devices and Systems

iScience, 2020
Neuromorphic devices and systems have attracted attention as next-generation computing due to their high efficiency in processing complex data. So far, they have been demonstrated using both machine-learning software and complementary metal-oxide ...
Min-Kyu Kim, Youngjun Park, Ik-Jyae Kim, Jang-Sik Lee   +3 more
doaj   +1 more source

Neuromorphic computing for content-based image retrieval.

PLoS ONE, 2022
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency.
Te-Yuan Liu, Ata Mahjoubfar, Daniel Prusinski, Luis Stevens   +3 more
doaj   +1 more source

Multi‐Scale Interface Engineering of MXenes for Multifunctional Sensory Systems

Advanced Functional Materials, EarlyView.
MXenes, as two‐dimensional transition metal carbides and nitrides, demonstrate remarkable capabilities for multifunctional sensing applications. This review systematically examines multi‐scale interface engineering approaches that enhance sensing performance, enable diverse detection functionalities, and improve system‐level compatibility in MXene ...
Jiaying Liao, Sin‐Yi Pang, Jianhua Hao
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

Accelerated Analog Neuromorphic Computing

, 2021
This paper presents the concepts behind the BrainScales (BSS) accelerated analog neuromorphic computing architecture. It describes the second-generation BrainScales-2 (BSS-2) version and its most recent in-silico realization, the HICANN-X Application Specific Integrated Circuit (ASIC), as it has been developed as part of the neuromorphic computing ...
Johannes Schemmel, Sebastian Billaudelle, Philipp Dauer, Johannes Weis   +3 more
openaire   +2 more sources

Thermally Engineered Sodium‐Embedded Alumina with Programmable Synaptic Plasticity for Neuromorphic Transistors

Advanced Functional Materials, EarlyView.
A fully transparent, all‐metal‐oxide neuromorphic transistor using a sodium‐embedded alumina (SEA) electrolyte is demonstrated. By precisely tuning the thermal annealing process, the chemical composition of the SEA layer is controlled, allowing for the deterministic realization of both short‐term and long‐term synaptic plasticity within the same device
Yonghyun Albert Kwon, Jaeyeon Kim, Dong Yeon Won, Saeyoun Park, Youngjae Yoo, Seongchan Kim, Dong‐Hwan Kim, Jeong Ho Cho   +7 more
wiley   +1 more source