Results 11 to 20 of about 245,739 (312)
AvA: Accelerated Virtualization of Accelerators [PDF]
Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, 2020 Applications are migrating en masse to the cloud, while accelerators such as GPUs, TPUs, and FPGAs proliferate in the wake of Moore's Law. These trends are in conflict: cloud applications run on virtual platforms, but existing virtualization techniques have not provided production-ready solutions for accelerators.
Hangchen Yu +3 more
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Applied Sciences, 2021 Generally, glow-discharge detectors (GDD), acting on miniature neon indicator lamps, and Schottky diode detectors serve as efficient, fast, and room-temperature millimeter wave (MMW)/THz detectors.
Adnan Haj Yahya +4 more
doaj +1 more source
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Training [PDF]
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020 DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit level and up to algorithm level. A python wrapper is developed to interface
Xiaochen Peng +4 more
semanticscholar +1 more source
Odd entanglement entropy and logarithmic negativity for thermofield double states
Journal of High Energy Physics, 2021 We investigate the time evolution of odd entanglement entropy (OEE) and logarithmic negativity (LN) for the thermofield double (TFD) states in free scalar quantum field theories using the covariance matrix approach.
Mostafa Ghasemi +2 more
doaj +1 more source
FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review [PDF]
IEEE Access, 2019 Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged and has demonstrated its ability and effectiveness in solving complex learning problems not possible before.
Ahmad Shawahna, S. M. Sait, A. El-Maleh
semanticscholar +1 more source
SoK: Fully Homomorphic Encryption Accelerators [PDF]
ACM Computing Surveys, 2022 Fully Homomorphic Encryption (FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the underlying polynomial computations with high computation complexity and ...
Junxue Zhang +5 more
semanticscholar +1 more source
Acceleration modules in linear induction accelerators [PDF]
Chinese Physics C, 2014 Linear Induction Accelerator (LIA) is a unique type of accelerator, which is capable to accelerate kilo-Ampere beam current to tens of MeV. The LIA acceleration modules, filled with ferrite or ferromagnetic toroid cores, can be conveniently stacked to obtain high energy. During the evolution of LIA, several models for the LIA acceleration module and the
Wang, Shaoheng, Deng, Jianjun
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Accelerators and the Accelerator Community [PDF]
Reviews of Accelerator Science and Technology, 2008 In this paper, standing back — looking from afar — and adopting a historical perspective, the field of accelerator science is examined. The subjects explored are: how it grew, what were the forces that made it what it is, where it is now, and what it is likely to be in the future. Clearly, many personal opinions, are offered in this process.
Malamud, Ernest, Sessler, Andrew
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International Symposium on Computer Architecture, 2022 Hardware specialization is a promising trend to sustain performance growth. Spatial hardware accelerators that employ specialized and hierarchical computation and memory resources have recently shown high performance gains for tensor applications such as
Size Zheng +9 more
semanticscholar +1 more source
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators [PDF]
ACM Computing Surveys, 2020 Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data.
S. Abadal +4 more
semanticscholar +1 more source