Keyword

hardware accelerators

Description: Specialized computing devices or circuits that execute particular tasks more efficiently than general-purpose processors.

Primary topic: Embedded Systems Design Techniques (28%)

Wikidata ID: wiki/Q600158

OpenAlex ID: keywords/hardware-accelerators

PyTorch: An Imperative Style, High-Performance Deep Learning Library

2019 · Paszke, Adam, Gross, Sam, et al. · arXiv (Cornell University) · 31,490

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Efficient Processing of Deep Neural Networks: A Tutorial and Survey

2017 · Vivienne Sze, Yu-Hsin Chen, et al. · Proceedings of the IEEE · 4,204

Q uantum ESPRESSO toward the exascale

2020 · Paolo Giannozzi, Oscar Baseggio, et al. · The Journal of Chemical Physics · 2,053

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EIE

2016 · Song Han, Xingyu Liu, et al. · ACM SIGARCH Computer Architecture News · 1,935

Equivalent-accuracy accelerated neural-network training using analogue memory

2018 · Stefano Ambrogio, Pritish Narayanan, et al. · Nature · 1,147

HAQ: Hardware-Aware Automated Quantization With Mixed Precision

2019 · Kuan Wang, Zhijian Liu, et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 973

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A scalable processing-in-memory accelerator for parallel graph processing

2015 · Junwhan Ahn, Sungpack Hong, et al. · Proceedings - International Symposium on Computer Architecture/Proceedings · 768

Dot-product engine for neuromorphic computing

2016 · Miao Hu, John Paul Strachan, et al. · Proceedings - ACM IEEE Design Automation Conference · 687

LegUp

2011 · Andrew Canis, Jongsok Choi, et al. · ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA) · 531

FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review

2018 · Ahmad Shawahna, Sadiq M. Sait, et al. · IEEE Access · 499

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CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

2008 · Svetlin A Manavski, Giorgio Valle · BMC Bioinformatics · 468

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A survey of FPGA-based accelerators for convolutional neural networks

2018 · Sparsh Mittal · Neural Computing and Applications · 431

System architecture for wireless sensor networks

2003 · David Culler, Jason Hill · 431

16.4 An 89TOPS/W and 16.3TOPS/mm2 All-Digital SRAM-Based Full-Precision Compute-In Memory Macro in 22nm for Machine-Learning Edge Applications

2021 · Yu-Der Chih, Po-Hao Lee, et al. · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · 380

Stripes: Bit-serial deep neural network computing

2016 · Patrick Judd, Jorge Albericio, et al. · Proceedings of the ... annual International Symposium on Microarchitecture/Proceedings of the annual International Symposium on Microarchitecture · 355

Computer vision algorithms and hardware implementations: A survey

2019 · Xin Feng, Youni Jiang, et al. · Integration · 350

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Building a high-performance, programmable secure coprocessor

1999 · Sean W Smith, Steve Weingart · Computer Networks · 336

LegUp

2013 · Andrew Canis, Jongsok Choi, et al. · ACM Transactions on Embedded Computing Systems · 305

OuterSPACE: An Outer Product Based Sparse Matrix Multiplication Accelerator

2018 · Subhankar Pal, Jonathan Beaumont, et al. · Proceedings - International Symposium on High-Performance Computer Architecture/Proceedings · 295

F1: A Fast and Programmable Accelerator for Fully Homomorphic Encryption

2021 · Nikola Samardzic, Axel Feldmann, et al. · 294

Analysis, fast algorithm, and VLSI architecture design for H.264/AVC intra frame coder

2005 · Yu-Wen Huang, Bing-Yu Hsieh, et al. · IEEE Transactions on Circuits and Systems for Video Technology · 292

Hardware-oriented Approximation of Convolutional Neural Networks

2016 · Gysel, Philipp, Motamedi, Mohammad, et al. · arXiv (Cornell University) · 287

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Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks

2018 · Weizhe Hua, Zhiru Zhang, et al. · Proceedings - ACM IEEE Design Automation Conference · 257

Beyond Individual Prompting Efficiency: A Socio-Technical Perspective on Computational Efficiency in AI-Assisted Software Engineering

2020 · Smite, Darja, Wivestad, Viggo Tellefsen, et al. · arXiv (Cornell University) · 256

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The true Processing In Memory accelerator

2019 · Fabrice Devaux · IEEE Hot Chips Symposium · 254

Works count: 8,625

Citation count: 108,400