Results 111 to 120 of about 4,441 (253)

The backpropagation algorithm implemented on spiking neuromorphic hardware

Nature Communications
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
Alpha Renner, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, Andrew Sornborger   +4 more
doaj   +1 more source

Neuromorphic Computing for IoT: Ultra-Low Power AI for Real-Time Intelligence

International Journal For Multidisciplinary Research
Neuromorphic computing presents a promising approach for enabling artificial intelligence capabilities in Internet of Things (IoT) devices with stringent power constraints. This paper explores the potential of neuromorphic architectures to deliver ultra-low power AI for real-time intelligence in IoT applications.
openaire   +1 more source

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

Advanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh, Ki‐bum Lee, Junghyeon Lee, Seunghee Cho, Malkeshkumar Patel, Hangfei Li, Yu Wen, Ye Zhou, Joondong Kim   +8 more
wiley   +1 more source

Unconventional Hysteretic Charge Filling in Moiré‐Reconstructed Helical Trilayer Graphene

Advanced Science, EarlyView.
In helical trilayer graphene, sequential twisting reconstructs the moiré landscape into periodic domains separated by aperiodic boundaries. Longitudinal transport reveals sweep‐direction‐dependent hysteresis, while the Hall response traces this behavior to hysteretic charge filling at the aperiodic boundaries.
Hangyeol Park, Junhyeok Oh, Rasoul Ghadimi, Chiranjit Mondal, Yungi Jeong, Won Beom Choi, Kenji Watanabe, Takashi Taniguchi, Bohm‐Jung Yang, Joonho Jang   +9 more
wiley   +1 more source

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

Nature Communications
CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased ...
Jaeseoung Park, Ashwani Kumar, Yucheng Zhou, Sangheon Oh, Jeong-Hoon Kim, Yuhan Shi, Soumil Jain, Gopabandhu Hota, Erbin Qiu, Amelie L. Nagle, Ivan K. Schuller, Catherine D. Schuman, Gert Cauwenberghs, Duygu Kuzum   +13 more
doaj   +1 more source

Electromagnetic Radiation Stimulated Learning in Perovskite Nickelates

Advanced Science, EarlyView.
ABSTRACT Biological plasticity refers to the ability of synapses to strengthen or weaken over time. These adaptive properties play a fundamental role in learning and memory, spanning many orders of magnitude in timescales. Short‐term plasticity (STP) arises from rapid correlative activity, while long‐term plasticity (LTP) is governed by slower ...
Ranjan Kumar Patel, Kabir Zama, Matthew Smart, Ramya Eathirajan, Ivan Seskar, Narayan Mandayam, Guangxin Ni, Martin Mönnigmann, Shriram Ramanathan   +8 more
wiley   +1 more source

Reconfigurable Selector‐Only Memory (SOM) for Scalable Neuromorphic Computing

Advanced Science, EarlyView.
ABSTRACT Highly scalable reconfigurable neuromorphic devices are critical for addressing continual‐learning challenges in artificial intelligence. However, the scalability of existing reconfigurable devices is severely constrained by limited operating margins and insufficient process maturity.
Jin‐Yu Wen, Chuan‐Qi Yi, Ya‐Ru Zhang, Bin‐Hao Wang, Zi‐Xuan Liu, Chun‐Yu Zhou, Hao Tong, Xiang‐Shui Miao   +7 more
wiley   +1 more source

Dual‐Mode Nanoporous SiO2 Memristors with Coexisting Volatile and Nonvolatile Dynamics for Reservoir Computing

Advanced Science, EarlyView.
A nanoporous SiO2 memristor enabling reconfigurable volatile and non‐volatile switching within a single device is demonstrated. The dual‐mode functionality supports both physical reservoir dynamics and synaptic weight storage, allowing unified hardware implementation of reservoir computing for temporal information processing, including image and ...
Bohao Ding, Tongjun Zhang, Li Shao, Nikolay Zhelev, Andrew L. Hector, Ruomeng Huang   +5 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Retinomorphic Visual Processing Enabled by Contact‐Engineered IGZO Optoelectronic Synaptic Memtransistors

Advanced Science, EarlyView.
Here, we present an optoelectronic synaptic memtransistor (OSMT) integrating photoresponsive IGZO with contact‐engineered HfO2, enabling electrically and optically tunable synaptic weights. The device demonstrates broad range of tunable conductance states and array‐level image processing, highlighting its potential for intelligent machine vision ...
Donghyun Kang, San Nam, Dayul Nam, In‐Soo Kim, Myung‐Gil Kim, Sung Kyu Park, Yong‐Hoon Kim   +6 more
wiley   +1 more source