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Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture
Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture
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Ata de Congresso 		Adicionar ao Meu Espaço

Simba: Scaling Deep-Learning Inference with Multi-Chip-Module-Based Architecture

Shao, Yakun Sophia ; Clemons, Jason ; Venkatesan, Rangharajan ; Zimmer, Brian ; Fojtik, Matthew ; Jiang, Nan ; Keller, Ben ; Klinefelter, Alicia ; Pinckney, Nathaniel ; Raina, Priyanka ; Tell, Stephen G. ; Zhang, Yanqing ; Dally, William J. ; Emer, Joel ; Gray, C. Thomas ; Khailany, Brucek ; Keckler, Stephen W.

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, p.14-27

New York, NY, USA: ACM

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DaDianNao: A Machine-Learning Supercomputer
DaDianNao: A Machine-Learning Supercomputer
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DaDianNao: A Machine-Learning Supercomputer

Chen, Yunji ; Luo, Tao ; Liu, Shaoli ; Zhang, Shijin ; He, Liqiang ; Wang, Jia ; Li, Ling ; Chen, Tianshi ; Xu, Zhiwei ; Sun, Ninghui ; Temam, Olivier

2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, 2014, p.609-622

Washington, DC, USA: IEEE Computer Society

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Architectures of flexible symmetric key crypto enginesaa survey: From hardware coprocessor to multi-crypto-processor system on chip
Architectures of flexible symmetric key crypto enginesaa survey: From hardware coprocessor to multi-crypto-processor system on chip
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Architectures of flexible symmetric key crypto enginesaa survey: From hardware coprocessor to multi-crypto-processor system on chip

Bossuet, Lilian ; Grand, Michael ; Gaspar, Lubos ; Fischer, Viktor ; Gogniat, Guy

ACM computing surveys, 2013-08, Vol.45 (4), p.1-32 [Periódico revisado por pares]

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A dynamically configurable coprocessor for convolutional neural networks
A dynamically configurable coprocessor for convolutional neural networks
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A dynamically configurable coprocessor for convolutional neural networks

Chakradhar, Srimat ; Sankaradas, Murugan ; Jakkula, Venkata ; Cadambi, Srihari

Proceedings of the 37th annual international symposium on Computer architecture, 2010, p.247-257

New York, NY, USA: ACM

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Transmuter: Bridging the Efficiency Gap using Memory and Dataflow Reconfiguration
Transmuter: Bridging the Efficiency Gap using Memory and Dataflow Reconfiguration
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Transmuter: Bridging the Efficiency Gap using Memory and Dataflow Reconfiguration

Pal, Subhankar ; Feng, Siying ; Park, Dong-hyeon ; Kim, Sung ; Amarnath, Aporva ; Yang, Chi-Sheng ; He, Xin ; Beaumont, Jonathan ; May, Kyle ; Xiong, Yan ; Kaszyk, Kuba ; Morton, John Magnus ; Sun, Jiawen ; O'Boyle, Michael ; Cole, Murray ; Chakrabarti, Chaitali ; Blaauw, David ; Kim, Hun-Seok ; Mudge, Trevor ; Dreslinski, Ronald

Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques, 2020, p.175-190

New York, NY, USA: ACM

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6
Pipelining a triggered processing element
Pipelining a triggered processing element
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Pipelining a triggered processing element

Repetti, Thomas J. ; Cerqueira, João P. ; Kim, Martha A. ; Seok, Mingoo

2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2017, p.96-108

New York, NY, USA: ACM

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7
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
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TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning

Kwon, Youngeun ; Lee, Yunjae ; Rhu, Minsoo

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, p.740-753

New York, NY, USA: ACM

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8
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges
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A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

Kleyko, Denis ; Rachkovskij, Dmitri ; Osipov, Evgeny ; Rahimi, Abbas

ACM computing surveys, 2023-09, Vol.55 (9), p.1-52, Article 175 [Periódico revisado por pares]

New York, NY: ACM

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9
Introduction to the Special Issue on Next-Generation On-Chip and Off-Chip Communication Architectures for Edge, Cloud and HPC
Introduction to the Special Issue on Next-Generation On-Chip and Off-Chip Communication Architectures for Edge, Cloud and HPC
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Introduction to the Special Issue on Next-Generation On-Chip and Off-Chip Communication Architectures for Edge, Cloud and HPC

Kim, John ; Krishna, Tushar

ACM journal on emerging technologies in computing systems, 2023-11, Vol.19 (4), p.1-1, Article 31 [Periódico revisado por pares]

New York, NY: ACM

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10
Towards General Purpose Acceleration by Exploiting Common Data-Dependence Forms
Towards General Purpose Acceleration by Exploiting Common Data-Dependence Forms
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Towards General Purpose Acceleration by Exploiting Common Data-Dependence Forms

Dadu, Vidushi ; Weng, Jian ; Liu, Sihao ; Nowatzki, Tony

Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019, p.924-939

New York, NY, USA: ACM

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