18 papers · ranked by Valyu relevance
Sathvik Redrouthu, Rishi Athavale
—Tensor algebra lies at the core of computational science and machine learning. Due to its high usage, entire libraries exist dedicated to improving its performance. Conventional tensor algebra performance boosts focus on algorithmic optimizations, which in turn lead to incremental improvements. In this paper, we…
Rong-Yang Sun, Tomonori Shirakawa, Hidehiko Kohshiro, D. N. Sheng + 1 more
RIKEN Center for Computational Science (R-CCS), Kobe, Hyogo, 650-0047, Japan 4 Quantum Computational Science Research Team, RIKEN Center for Quantum Computing (RQC), Wako, Saitama, 351-0198, Japan 5 Computational Condensed Matter Physics Laboratory, RIKEN Cluster for Pioneering Research (CPR), Saitama 351-0198, Japan 6…
Xiangyan Meng, Nuannuan Shi, Guojie Zhang, Junshen Li + 6 more
'Shiyou Sun' 'Yichen Shen' 'Wei Li' 'Ninghua Zhu' 'Ming Li'] The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing hardware. Benefiting from the available optical multidimensional information entropy, optical intelligent computing is…
Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert + 9 more
'Bernardino Romera-Paredes' 'Mohammadamin Barekatain' 'Alexander Novikov' 'Francisco J. R. Ruiz' 'Julian Schrittwieser' 'Grzegorz Swirszcz' 'David Silver' 'Demis Hassabis' 'Pushmeet Kohli'] Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall…
Aleksandr Berezutskii, Minzhao Liu, Atithi Acharya, Roman Ellerbrock + 24 more
'Johnnie Gray' 'Reza Haghshenas' 'Zichang He' 'Abid Khan' 'Viacheslav Kuzmin' 'Dmitry Lyakh' 'Danylo Lykov' 'Salvatore Mandrà' 'Christopher Mansell' 'Alexey Melnikov' 'Artem Melnikov' 'Vladimir Mironov' 'Dmitry Morozov' 'Florian Neukart' 'Alberto Nocera' 'Michael A. Perlin' 'Michael Perelshtein' 'Matthew Steinberg'…
Bin Qi, Wensheng Zhang, Lei Zhang, Xingwang Li + 3 more
'Kefeng Guo'] The spectrum situation awareness problem in space-air-ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can…
Sajid Mehmood, Aqleema Arooj, Ahmad Sami Al-Shamayleh, Samera Batool + 4 more
The high rate of increase in the deep learning tasks as well as heterogeneous computing systems necessitates compilers that achieve low compile time and high performance. The current state of the art in the use of tensor compilers is based on exhaustive search, a slow and prohibitive process, or heuristics at the…
Erdal Mutlu, Ruiqin Tian, Bin Ren, Sriram Krishnamoorthy + 3 more
'Roberto Gioiosa' 'Jacques A. Pienaar' 'Gökçen Kestor'] The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations.…
Beheshteh T. Rakhshan, Guillaume Rabusseau
High-dimensional data arise naturally in many areas of science and engineering, including machine learning, signal processing, computational physics, and statistics. Such data are often represented as tensors, multi-dimensional generalizations of matrices. While tensors provide a natural representation for multi-modal…
Salman Ahmadi‐Asl, Anh Huy Phan, Andrzej Cichocki, Anastasia Sozykina + 3 more
'Anastasia Sozykina' 'Zaher Al Aghbari' 'Jun Wang' 'Ivan Oseledets'] In this paper, we propose a new adaptive cross algorithm for computing a low tubal rank approximation of third-order tensors, with less memory and lower computational complexity than the truncated tensor SVD (t-SVD). This makes it applicable for…
Alejandro Mata Ali, Iñigo Perez Delgado, Aitor Moreno-Fernández-de-Leceta
'Aitor Moreno-Fernández-de-Leceta'] In this paper we present a study of the applicability and feasibility of quantuminspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such…
José Ramón Pareja Monturiol, David Pérez-Garcı́a, Alejandro Pozas-Kerstjens
'Alejandro Pozas-Kerstjens'] > Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures. To ease the integration of tensor networks in machine learning…
Andor Menczer, Örs Legeza
Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures Authors: ['Andor Menczer' 'Örs Legeza'] The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for high performance computing…
Hongwei Wang, Guangwei Hu
In artificial neural networks, data structures usually exist in the form of vectors, matrices, or higher-dimensional tensors. However, traditional electronic computing architectures are limited by the bottleneck of separation of storage and computing, making it difficult to efficiently handle large-scale tensor…
Yajushi Khurana, Keisuke Ishihara
Three-dimensional biological morphologies encode functional and physiological state, yet the directional, orientational, and topological properties of these shapes are rarely captured by morphometric tools available for bioimage analysis. Minkowski tensors are mathematically rigorous tensor-valued measures that encode…
Authors not listed
The era of exascale computing presents both exciting opportunities and unique challenges for quantum mechanical simulations. While the transition from petaflops to exascale computing has been marked by a steady increase in computational power, the shift towards heterogeneous architectures, particularly the dominant…
Jing Yang Zhou, Chanwoo Chun, Ajay Subramanian, Eero P. Simoncelli
Internal representations are not uniquely identifiable from perceptual measurements: different representations can generate identical perceptual predictions, and similar representations may predict dissimilar percepts. Here, we generalize a previous method (“Eigendistortions” – Berardino et al., 2017) to enable…
David S. Cerutti, Rafal Wiewiora, Simon Boothroyd, Woody Sherman
The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, multicore central processor units (CPUs) and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM…