Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU126431" target="_blank" >RIV/00216305:26230/17:PU126431 - isvavai.cz</a>

Result on the web

<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11474" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11474</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISVLSI.2017.124" target="_blank" >10.1109/ISVLSI.2017.124</a>

Result language

angličtina

Original language name

Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap

Original language description

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 datapaths 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20206 - Computer hardware and architecture

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2017 IEEE Computer Society Annual Symposium on VLSI

ISBN

978-1-5090-6762-6

ISSN

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e-ISSN

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Number of pages

6

Pages from-to

627-632

Publisher name

IEEE Computer Society Press

Place of publication

Los Alamitos

Event location

Bochum

Event date

Jul 3, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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