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11
SparTen: A Sparse Tensor Accelerator for Convolutional Neural Networks
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Ata de Congresso
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SparTen: A Sparse Tensor Accelerator for Convolutional Neural Networks

Gondimalla, Ashish ; Chesnut, Noah ; Thottethodi, Mithuna ; Vijaykumar, T. N.

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, p.151-165

New York, NY, USA: ACM

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12
SNrram: an efficient sparse neural network computation architecture based on resistive random-access memory
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SNrram: an efficient sparse neural network computation architecture based on resistive random-access memory

Wang, Peiqi ; Ji, Yu ; Hong, Chi ; Lyu, Yongqiang ; Wang, Dongsheng ; Xie, Yuan

Proceedings of the 55th Annual Design Automation Conference, 2018, p.1-6

New York, NY, USA: ACM

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13
Exploring the tradeoffs between programmability and efficiency in data-parallel accelerators
Material Type:
Ata de Congresso
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Exploring the tradeoffs between programmability and efficiency in data-parallel accelerators

Lee, Yunsup ; Avizienis, Rimas ; Bishara, Alex ; Xia, Richard ; Lockhart, Derek ; Batten, Christopher ; Asanović, Krste

2011 38th Annual International Symposium on Computer Architecture (ISCA), 2011, p.129-140

New York, NY, USA: ACM

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14
Accelerating Blockchain Applications on IoT Architecture Models - Solutions and Drawbacks
Material Type:
Artigo
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Accelerating Blockchain Applications on IoT Architecture Models - Solutions and Drawbacks

Kromes, Roland ; Verdier, François

Journal of the ACM, 2023-09, Vol.1 [Periódico revisado por pares]

Association for Computing Machinery

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15
Large-scale Simulations of Peridynamics on Sunway Taihulight Supercomputer
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Ata de Congresso
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Large-scale Simulations of Peridynamics on Sunway Taihulight Supercomputer

Li, Xinyuan ; Ye, Huang ; Zhang, Jian

Proceedings of the 49th International Conference on Parallel Processing, 2020, p.1-11

New York, NY, USA: ACM

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16
A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
Material Type:
Artigo
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A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions

Ren, Pengzhen ; Xiao, Yun ; Chang, Xiaojun ; Huang, Po-yao ; Li, Zhihui ; Chen, Xiaojiang ; Wang, Xin

ACM computing surveys, 2021-07, Vol.54 (4), p.1-34 [Periódico revisado por pares]

Baltimore: Association for Computing Machinery

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17
DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator
Material Type:
Ata de Congresso
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DNNExplorer: A Framework for Modeling and Exploring a Novel Paradigm of FPGA-based DNN Accelerator

Zhang, Xiaofan ; Ye, Hanchen ; Wang, Junsong ; Lin, Yonghua ; Xiong, Jinjun ; Hwu, Wen-Mei ; Chen, Deming

2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 2020, p.1-9

Association on Computer Machinery

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18
A scalable processing-in-memory accelerator for parallel graph processing
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Ata de Congresso
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A scalable processing-in-memory accelerator for parallel graph processing

Ahn, Junwhan ; Hong, Sungpack ; Yoo, Sungjoo ; Mutlu, Onur ; Choi, Kiyoung

Proceedings of the 42nd Annual International Symposium on Computer Architecture, 2015, p.105-117

New York, NY, USA: ACM

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19
Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators
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Ata de Congresso
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Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

Yang, Xuan ; Gao, Mingyu ; Liu, Qiaoyi ; Setter, Jeff ; Pu, Jing ; Nayak, Ankita ; Bell, Steven ; Cao, Kaidi ; Ha, Heonjae ; Raina, Priyanka ; Kozyrakis, Christos ; Horowitz, Mark

Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, 2020, p.369-383

New York, NY, USA: ACM

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20
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
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EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM

Koppula, Skanda ; Orosa, Lois ; Yağlıkçı, A. Giray ; Azizi, Roknoddin ; Shahroodi, Taha ; Kanellopoulos, Konstantinos ; Mutlu, Onur

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, p.166-181

New York, NY, USA: ACM

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