Publication detail

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

SHAFIQUE, M. HAFIZ, R. JAVED, M. ABBAS, S. SEKANINA, L. VAŠÍČEK, Z. MRÁZEK, V.

Original Title

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

Type

conference paper

Language

English

Original 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 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.

Keywords

machine learning, approximate computing, deep learning, neural networks, energy efficiency

Authors

SHAFIQUE, M.; HAFIZ, R.; JAVED, M.; ABBAS, S.; SEKANINA, L.; VAŠÍČEK, Z.; MRÁZEK, V.

Released

11. 7. 2017

Publisher

IEEE Computer Society Press

Location

Los Alamitos

ISBN

978-1-5090-6762-6

Book

2017 IEEE Computer Society Annual Symposium on VLSI

Pages from

627

Pages to

632

Pages count

6

URL

https://www.fit.vut.cz/research/publication/11474/

BibTex

@inproceedings{BUT144454,
  author="Muhammad {Shafique} and Rehan {Hafiz} and Muhammad Usama {Javed} and Sarmad {Abbas} and Lukáš {Sekanina} and Zdeněk {Vašíček} and Vojtěch {Mrázek}",
  title="Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap",
  booktitle="2017 IEEE Computer Society Annual Symposium on VLSI",
  year="2017",
  pages="627--632",
  publisher="IEEE Computer Society Press",
  address="Los Alamitos",
  doi="10.1109/ISVLSI.2017.124",
  isbn="978-1-5090-6762-6",
  url="https://www.fit.vut.cz/research/publication/11474/"
}

Documents

isvlsi17.pdf