16 papers · ranked by Valyu relevance
Gabriel Wlazłowski, Michael McNeil Forbes, Saptarshi Rajan Sarkar, Andreas Marek + 2 more
'Andreas Marek' 'Maciej Szpindler' 'Cristina H Amon'] Title: Abstract Ultracold atoms provide a platform for analog quantum computer capable of simulating the quantum turbulence that underlies puzzling phenomena like pulsar glitches in rapidly spinning neutron stars. Unlike other platforms like liquid helium, ultracold…
Jari Pronold, Jakob Jordan, Brian J. N. Wylie, Itaru Kitayama + 2 more
'Markus Diesmann' 'Susanne Kunkel'] Generic simulation code for spiking neuronal networks spends the major part of the time in the phase where spikes have arrived at a compute node and need to be delivered to their target neurons. These spikes were emitted over the last interval between communication steps by source…
Xuzhen He, Viacheslav Kovtun
The recent dramatic progress in machine learning is partially attributed to the availability of high-performant computers and development tools. The accelerated linear algebra (XLA) compiler is one such tool that automatically optimises array operations (mostly fusion to reduce memory operations) and compiles the…
János Végh, Ádám József Berki
In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between…
Benedikt Feldotto, Jochen Martin Eppler, Cristian Jimenez-Romero, Christopher Bignamini + 18 more
'Christopher Bignamini' 'Carlos Enrique Gutierrez' 'Ugo Albanese' 'Eloy Retamino' 'Viktor Vorobev' 'Vahid Zolfaghari' 'Alex Upton' 'Zhe Sun' 'Hiroshi Yamaura' 'Morteza Heidarinejad' 'Wouter Klijn' 'Abigail Morrison' 'Felipe Cruz' 'Colin McMurtrie' 'Alois C. Knoll' 'Jun Igarashi' 'Tadashi Yamazaki' 'Kenji Doya' 'Fabrice…
Rene Miedema, Christos Strydis
Introduction In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2)…
Arne Heittmann, Georgia Psychou, Guido Trensch, Charles E. Cox + 3 more
This article employs the new IBM INC-3000 prototype FPGA-based neural supercomputer to implement a widely used model of the cortical microcircuit. With approximately 80,000 neurons and 300 Million synapses this model has become a benchmark network for comparing simulation architectures with regard to performance. To…
Hammad Ather, Sophie Berkman, Giuseppe Cerati, Matti J. Kortelainen + 9 more
Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this…
Zhikun Wu, Honghui Shang, Yangjun Wu, Zhongcheng Zhang + 5 more
'Yuyang Zhang' 'Yucheng Ouyang' 'Huimin Cui' 'Xiaobing Feng'] We have proposed, for the first time, an OpenCL implementation for the all-electron density-functional perturbation theory (DFPT) calculations in FHI-aims, which can effectively compute all its time-consuming simulation stages, i.e., the real-space…
Ayca Kirimtat, Ondrej Krejcar, Fow-Sen Choa
The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image…
Guido Trensch, Abigail Morrison
Despite the great strides neuroscience has made in recent decades, the underlying principles of brain function remain largely unknown. Advancing the field strongly depends on the ability to study large-scale neural networks and perform complex simulations. In this context, simulations in hyper-real-time are of high…
Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke Vennemo + 8 more
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease. This process goes hand in hand with advancements in the theory of neuronal networks and increasing availability of detailed anatomical data on brain connectivity. Large-scale…
Jingjing Wang, Xiaoyu Liu, Yuxin Li, Ruina Mao + 1 more
Microstructure simulations of continuous casting billets are vital for understanding solidification mechanisms and optimizing process parameters. However, the commonly used CA (Cellular Automaton) model is limited by grid anisotropy, which affects the accuracy of dendrite morphology simulations. While the DCSA…
Ludovico Rella
This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphics processing units (GPUs) have been instrumental in the explosion of…
Song Wang, Qiushuang Yu, Tiantian Xie, Cheng Ma + 1 more
The decentralized manycore architecture is broadly adopted by neuromorphic chips for its high computing parallelism and memory locality. However, the fragmented memories and decentralized execution make it hard to deploy neural network models onto neuromorphic hardware with high resource utilization and processing…
Kevin Kauth, Tim Stadtmann, Vida Sobhani, Tobias Gemmeke
Introduction Research in the field of computational neuroscience relies on highly capable simulation platforms. With real-time capabilities surpassed for established models like the cortical microcircuit, it is time to conceive next-generation systems: neuroscience simulators providing significant acceleration, even…