A Low Power Branch Prediction for Deep Learning on RISC-V Processor

Mapping Intimacies ◽

10.1109/asap52443.2021.00037 ◽

2021 ◽

Author(s):

Mingjian Sun ◽

Yuan Li ◽

Song Chen ◽

Yi Kang

Keyword(s):

Deep Learning ◽

Low Power ◽

Branch Prediction

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Branch prediction techniques for low-power VLIW processors

Proceedings of the 13th ACM Great Lakes Symposium on VLSI - GLSVLSI '03 ◽

10.1145/764808.764866 ◽

2003 ◽

Author(s):

G. Palermo ◽

M. Sam ◽

C. Silvan ◽

V. Zaccari ◽

R. Zafalo

Keyword(s):

Low Power ◽

Branch Prediction ◽

Vliw Processors ◽

Prediction Techniques

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Deep Learning Algorithms for Emotion Recognition on Low Power Single Board Computers

Lecture Notes in Computer Science - Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction ◽

10.1007/978-3-030-20984-1_6 ◽

2019 ◽

pp. 59-70 ◽

Author(s):

Venkatesh Srinivasan ◽

Sascha Meudt ◽

Friedhelm Schwenker

Keyword(s):

Deep Learning ◽

Low Power ◽

Emotion Recognition ◽

Learning Algorithms

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Low power branch prediction for embedded application processors

Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design - ISLPED '10 ◽

10.1145/1840845.1840860 ◽

2010 ◽

Author(s):

Nadav Levison ◽

Shlomo Weiss

Keyword(s):

Low Power ◽

Branch Prediction ◽

Embedded Application

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Energy-performance design exploration of a low-power microprogrammed deep-learning accelerator

2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) ◽

10.23919/date.2018.8342185 ◽

2018 ◽

Author(s):

Giulia Santoro ◽

Mario R. Casu ◽

Valentino Peluso ◽

Andrea Calimera ◽

Massimo Alioto

Keyword(s):

Deep Learning ◽

Low Power ◽

Energy Performance ◽

Design Exploration

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Collision prediction for a low power wide area network using deep learning methods

Journal of Communications and Networks ◽

10.1109/jcn.2020.000017 ◽

2020 ◽

Vol 22 (3) ◽

pp. 205-214 ◽

Author(s):

Shengmin Cui ◽

Inwhee Joe

Keyword(s):

Deep Learning ◽

Low Power ◽

Wide Area ◽

Area Network ◽

Wide Area Network ◽

Learning Methods ◽

Collision Prediction

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Stochastic Computing for Low-Power and High-Speed Deep Learning on FPGA

2019 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2019.8702248 ◽

2019 ◽

Author(s):

Corey Lammie ◽

Mostafa Rahimi Azghadi

Keyword(s):

Deep Learning ◽

Low Power ◽

Stochastic Computing

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Optimal Design Methods to Transform 3D NAND Flash into a High-Density, High-Bandwidth and Low-Power Nonvolatile Computing in Memory (nvCIM) Accelerator for Deep-Learning Neural Networks (DNN)

2019 IEEE International Electron Devices Meeting (IEDM) ◽

10.1109/iedm19573.2019.8993652 ◽

2019 ◽

Author(s):

Hang-Ting Lue ◽

Po-Kai Hsu ◽

Ming-Liang Wei ◽

Teng-Hao Yeh ◽

Pei-Ying Du ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Optimal Design ◽

Low Power ◽

Design Methods ◽

High Density ◽

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Low-power, high-performance analog neural branch prediction

2008 41st IEEE/ACM International Symposium on Microarchitecture ◽

10.1109/micro.2008.4771812 ◽

2008 ◽

Author(s):

Renee St. Amant ◽

Daniel A. Jimenez ◽

Doug Burger

Keyword(s):

Low Power ◽

High Performance ◽

Branch Prediction

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Deep learning for human activity recognition: A resource efficient implementation on low-power devices

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN) ◽

10.1109/bsn.2016.7516235 ◽

2016 ◽

Author(s):

Daniele Ravi ◽

Charence Wong ◽

Benny Lo ◽

Guang-Zhong Yang

Keyword(s):

Deep Learning ◽

Low Power ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Efficient Implementation ◽

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DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices

2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) ◽

10.1109/ipsn.2016.7460664 ◽

2016 ◽

Author(s):

Nicholas D. Lane ◽

Sourav Bhattacharya ◽

Petko Georgiev ◽

Claudio Forlivesi ◽

Lei Jiao ◽

...

Keyword(s):

Deep Learning ◽

Low Power ◽

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