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pro vyhledávání: '"Davies, Michael P"'
While GPU clusters are the de facto choice for training large deep neural network (DNN) models today, several reasons including ease of workflow, security and cost have led to efforts investigating whether CPUs may be viable for inference in routine
Externí odkaz:
http://arxiv.org/abs/2403.07221
Motivated by the end of Moore's Law and Dennard Scaling which necessitate architectural efficiency as the means for improved capability for the next decade or two, this paper introduces a new data-rich paradigm of chip design for the semi-conductor i
Externí odkaz:
http://arxiv.org/abs/2312.13428
Nucleation in small volumes of water has garnered renewed interest due to the relevance of pore condensation and freezing under conditions of low partial pressures of water, such as in the upper troposphere. Molecular simulations can in principle pro
Externí odkaz:
http://arxiv.org/abs/2306.12903
Autor:
Davies, Michael Benedict, Rosu-Finsen, Alexander, Salzmann, Christoph G., Michaelides, Angelos
Low-density amorphous ice (LDA) is one of the most common solid materials in the Universe and a key material for understanding the many famous anomalies of liquid water. Yet, despite its significance and its discovery dating nearly 90 years, the stru
Externí odkaz:
http://arxiv.org/abs/2305.03057
Violet: Architecturally Exposed Orchestration, Movement, and Placement for Generalized Deep Learning
Deep learning and hardware for it has garnered immense academic and industry interest in the past 5 years, with many novel proposals. However, the state-of-art remains NVIDIA's TensorCore-based systems that provide top-of-line performance and coverag
Externí odkaz:
http://arxiv.org/abs/2112.02204
Autor:
Skuratovs, Nikolajs, Davies, Michael
Many modern imaging applications can be modeled as compressed sensing linear inverse problems. When the measurement operator involved in the inverse problem is sufficiently random, denoising Scalable Message Passing (SMP) algorithms have a potential
Externí odkaz:
http://arxiv.org/abs/2105.07086
Autor:
Skuratovs, Nikolajs, Davies, Michael
The Recently proposed Vector Approximate Message Passing (VAMP) algorithm demonstrates a great reconstruction potential at solving compressed sensing related linear inverse problems. VAMP provides high per-iteration improvement, can utilize powerful
Externí odkaz:
http://arxiv.org/abs/2011.01369
Autor:
Legros, Quentin, Tachella, Julian, Tobin, Rachael, McCarthy, Aongus, Meignen, Sylvain, Buller, Gerald S., Altmann, Yoann, McLaughlin, Stephen, Davies, Michael E.
In this paper, we present a new algorithm for fast, online 3D reconstruction of dynamic scenes using times of arrival of photons recorded by single-photon detector arrays. One of the main challenges in 3D imaging using single-photon lidar in practica
Externí odkaz:
http://arxiv.org/abs/2004.09211
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