Results 21 to 30 of about 14,001 (185)

Architecture and Design of a Spiking Neuron Processor Core Towards the Design of a Large-scale Event-Driven 3D-NoC-based Neuromorphic Processor [PDF]

SHS Web of Conferences, 2020
Neuromorphic computing tries to model in hardware the biological brain which is adept at operating in a rapid, real-time, parallel, low power, adaptive and fault-tolerant manner within a volume of 2 liters.
Ogbodo Mark, Dang Khanh, Tomohide Fukuchi, Abdallah Abderazek   +3 more
doaj   +1 more source

Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor [PDF]

, 2018
Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building ...
Glatz, Sebastian, Kreiser, Raphaela, Martel, Julien N. P., Qiao, Ning, Sandamirskaya, Yulia   +4 more
core   +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

The Intel neuromorphic DNS challenge

Neuromorphic Computing and Engineering, 2023
A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
Jonathan Timcheck, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, Adam Kupryjanow, Garrick Orchard, Lukasz Pindor, Timothy Shea, Mike Davies   +7 more
doaj   +1 more source

Bio‐Voltage Memristors: From Physical Mechanisms to Neuromorphic Interfaces

Advanced Electronic Materials, 2023
With the rapid development of emerging artificial intelligence technology, brain–computer interfaces are gradually moving from science fiction to reality, which has broad application prospects in the field of intelligent robots.
Saisai Wang, Rui Wang, Yaxiong Cao, Xiaohua Ma, Hong Wang, Yue Hao   +5 more
doaj   +1 more source

Halide perovskite for low‐power consumption neuromorphic devices

EcoMat, 2021
The rapid emergency of data science, information technology, and artificial intelligence (AI) relies on massive data processing with high computing efficiency and low power consumption.
Itaru Raifuku, Yung‐Pin Chao, Hong‐Hsueh Chen, Chen‐Fu Lin, Pei‐En Lin, Li‐Chung Shih, Kuan‐Ting Chen, Jung‐Yao Chen, Jen‐Sue Chen, Peter Chen   +9 more
doaj   +1 more source

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

Science and Technology of Advanced Materials, 2023
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data.
Yi Zhang, Zhuohui Huang, Jie Jiang
doaj   +1 more source

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective [PDF]

, 2019
On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential.
Abbott, Bartolozzi, Bartolozzi, Benjamin, Bi, Bohte, Detorakis, Faisal, Fouda, Fouda, Furber, Gerstner, Gerstner, Goldberg, Gütig, Hinton, Hopfield, Huayaney, Hyvärinen, Ielmini, Indiveri, Isomura, Jolivet, Katz, Kim, Levy, Li, Lillicrap, Maass, Mead, Merolla, Moreno-Bote, Naous, Neftci, Neftci, Neftci, Paille, Park, Pfister, Prezioso, Puglisi, Qiao, Querlioz, Savin, Saïghi, Schultz, Shouval, Tiago, Urbanczik, Wan, Wang, Williams, Woo, Xie, Yarom, Yu, Yu   +56 more
core   +2 more sources

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

PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network

, 2020
We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two key contributions.
Adiraju, Prathyusha, Balaji, Adarsha, Catthoor, Francky, Das, Anup, Dutt, Nikil D., Kashyap, Hirak J., Krichmar, Jeffrey L.   +6 more
core   +1 more source