Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{7987592,
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author = "Muhammad Shafique and Rehan Hafiz and
Muhammad Usama Javed and Sarmad Abbas and Lukas Sekanina and
Zdenek Vasicek and Vojtech Mrazek",
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title = "Adaptive and Energy-Efficient Architectures for
Machine Learning: Challenges, Opportunities, and
Research Roadmap",
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booktitle = "2017 IEEE Computer Society Annual Symposium on VLSI
(ISVLSI)",
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year = "2017",
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pages = "627--632",
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address = "Bochum, Germany",
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month = "3-5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ISVLSI.2017.124",
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size = "6 pages",
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abstract = "Gigantic rates of data production in the era of Big
Data, Internet of Thing (IoT)/Internet of Everything
(IoE), and Cyber Physical Systems (CSP) pose
incessantly escalating demands for massive data
processing, storage, and transmission while
continuously interacting with the physical world under
unpredictable, harsh, and energy-/power-constrained
scenarios. Therefore, such systems need to support not
only the high performance capabilities at tight
power/energy envelop, but also need to be
intelligent/cognitive, self-learning, and robust. As a
result, a hype in the artificial intelligence research
(e.g., deep learning and other machine learning
techniques) has surfaced in numerous communities. This
paper discusses the challenges and opportunities for
building energy-efficient and adaptive architectures
for machine learning. In particular, we focus on
brain-inspired emerging computing paradigms, such as
approximate computing; that can further reduce the
energy requirements of the system. First, we guide
through an approximate computing based methodology for
development of energy-efficient accelerators,
specifically for convolutional Deep Neural Networks
(DNNs). We show that in-depth analysis of data paths of
a DNN allows better selection of Approximate Computing
modules for energy-efficient accelerators. Further, we
show that a multi-objective evolutionary algorithm can
be used to develop an adaptive machine learning system
in hardware. At the end, we summarize the challenges
and the associated research roadmap that can aid in
developing energy-efficient and adaptable hardware
accelerators for machine learning.",
- }
Genetic Programming entries for
Muhammad Shafique
Rehan Hafiz
Muhammad Usama Javed
Sarmad Abbas
Lukas Sekanina
Zdenek Vasicek
Vojtech Mrazek
Citations