Results 11 to 20 of about 2,883,478 (184)

Low-Power Memristor for Neuromorphic Computing: From Materials to Applications. [PDF]

Nanomicro Lett
As an emerging memory device, memristor shows great potential in neuromorphic computing applications due to its advantage of low power consumption. This review paper focuses on the application of low-power-based memristors in various aspects. The concept
Xia Z, Sun X, Wang Z, Meng J, Jin B, Wang T.   +5 more
europepmc   +2 more sources

The neurobench framework for benchmarking neuromorphic computing algorithms and systems. [PDF]

Nat Commun
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to ...
Yik J, Van den Berghe K, den Blanken D, Bouhadjar Y, Fabre M, Hueber P, Ke W, Khoei MA, Kleyko D, Pacik-Nelson N, Pierro A, Stratmann P, Sun PV, Tang G, Wang S, Zhou B, Ahmed SH, Vathakkattil Joseph G, Leto B, Micheli A, Mishra AK, Lenz G, Sun T, Ahmed Z, Akl M, Anderson B, Andreou AG, Bartolozzi C, Basu A, Bogdan P, Bohte S, Buckley S, Cauwenberghs G, Chicca E, Corradi F, de Croon G, Danielescu A, Daram A, Davies M, Demirag Y, Eshraghian J, Fischer T, Forest J, Fra V, Furber S, Furlong PM, Gilpin W, Gilra A, Gonzalez HA, Indiveri G, Joshi S, Karia V, Khacef L, Knight JC, Kriener L, Kubendran R, Kudithipudi D, Liu SC, Liu YH, Ma H, Manohar R, Margarit-Taulé JM, Mayr C, Michmizos K, Muir DR, Neftci E, Nowotny T, Ottati F, Ozcelikkale A, Panda P, Park J, Payvand M, Pehle C, Petrovici MA, Posch C, Renner A, Sandamirskaya Y, Schaefer CJS, van Schaik A, Schemmel J, Schmidgall S, Schuman C, Seo JS, Sheik S, Shrestha SB, Sifalakis M, Sironi A, Stewart K, Stewart M, Stewart TC, Timcheck J, Tömen N, Urgese G, Verhelst M, Vineyard CM, Vogginger B, Yousefzadeh A, Zohora FT, Frenkel C, Reddi VJ.   +99 more
europepmc   +2 more sources

Physics for neuromorphic computing [PDF]

Nature Reviews Physics, 2020
Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks.
Danijela Marković, A. Mizrahi, D. Querlioz, J. Grollier   +3 more
semanticscholar   +3 more sources

Bio-plausible reconfigurable spiking neuron for neuromorphic computing. [PDF]

Sci Adv
Biological neurons use diverse temporal expressions of spikes to achieve efficient communication and modulation of neural activities. Nonetheless, existing neuromorphic computing systems mainly use simplified neuron models with limited spiking behaviors ...
Xiao Y, Liu Y, Zhang B, Chen P, Zhu H, He E, Zhao J, Huo W, Jin X, Zhang X, Jiang H, Ma, Zheng Q, Tang H, Lin P, Kong W, Pan G.   +16 more
europepmc   +2 more sources

Neuromorphic quantum computing

Physical Review E, 2022
9 pages, 8 ...
Pehle, Christian, Wetterich, Christof
openaire   +3 more sources

Neuromorphic Computing

Elements, 2022
In the face of increasingly large computational demands and the impending halt to Moore's law, the semiconductor industry has been forced to re-evaluate the traditional computing paradism. Central to this re-evaluation has been the novel development of neuromorphic computing - an approach that, at its core, seeks to replicate the brain in silicon ...
null Elements, Will Riherd
openaire   +1 more source

Neuromorphic Computing

The Electrochemical Society Interface, 2023
Neuromorphic computing is a rapidly emerging field that seeks to emulate the computational principles of the brain using novel materials and devices. While traditional computing architectures, such as the von Neumann architecture, have experienced exponential improvements in computational power due to the continuous shrinkage of transistor technology ...
  +4 more sources

Quantum neuromorphic computing [PDF]

Applied Physics Letters, 2020
Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers.
Marković, Danijela, Grollier, Julie
openaire   +3 more sources

Photonics for artificial intelligence and neuromorphic computing [PDF]

Nature Photonics, 2020
Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class ...
B. Shastri, A. Tait, T. F. D. Lima, Wolfram H. P. Pernice, H. Bhaskaran, C. Wright, P. Prucnal   +6 more
semanticscholar   +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