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pro vyhledávání: '"Trefzer, Martin"'
Reservoir Computing is an Unconventional Computation model to perform computation on various different substrates, such as RNNs or physical materials. The method takes a "black-box" approach, training only the outputs of the system it is built on. As
Externí odkaz:
http://arxiv.org/abs/2405.06561
Autor:
Walter, Andrew, Wu, Shimeng, Tyrrell, Andy M., McDaid, Liam, McElholm, Malachy, Sumithran, Nidhin Thandassery, Harkin, Jim, Trefzer, Martin A.
Artificial Neural Networks (ANNs) are one of the most widely employed forms of bio-inspired computation. However the current trend is for ANNs to be structurally homogeneous. Furthermore, this structural homogeneity requires the application of comple
Externí odkaz:
http://arxiv.org/abs/2403.16327
Autor:
Allwood, Dan A, Ellis, Matthew O A, Griffin, David, Hayward, Thomas J, Manneschi, Luca, Musameh, Mohammad F KH, O'Keefe, Simon, Stepney, Susan, Swindells, Charles, Trefzer, Martin A, Vasilaki, Eleni, Venkat, Guru, Vidamour, Ian, Wringe, Chester
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advan
Externí odkaz:
http://arxiv.org/abs/2212.04851
While state-of-the-art development in CNN topology, such as VGGNet and ResNet, have become increasingly accurate, these networks are computationally expensive involving billions of arithmetic operations and parameters. To improve the classification a
Externí odkaz:
http://arxiv.org/abs/2106.14776
To tackle the complexity of state-of-the-art electronic systems, silicon foundries continuously shrink the technology nodes and electronic design automation (EDA) vendors offer hierarchical design flows to decompose systems into smaller blocks. Howev
Externí odkaz:
http://arxiv.org/abs/2105.11248
Modern electronic design automation (EDA) tools can handle the complexity of state-of-the-art electronic systems by decomposing them into smaller blocks or cells, introducing different levels of abstraction and staged design flows. However, throughou
Externí odkaz:
http://arxiv.org/abs/2105.10410
Autor:
Dale, Matthew, Griffin, David, Evans, Richard F. L., Jenkins, Sarah, O'Keefe, Simon, Sebald, Angelika, Stepney, Susan, Torre, Fernando, Trefzer, Martin
Publikováno v:
International Journal of Unconventional Computing, 19(1):63-92, 2024
Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, ne
Externí odkaz:
http://arxiv.org/abs/2101.12700
Akademický článek
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Publikováno v:
Proc.Roy.Soc.A 475(2226), 2019
The Reservoir Computing (RC) framework states that any non-linear, input-driven dynamical system (the reservoir) exhibiting properties such as a fading memory and input separability can be trained to perform computational tasks. This broad inclusion
Externí odkaz:
http://arxiv.org/abs/1810.07135
Akademický článek
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