Publication Details
Adaptive and Energy-Efficient Architectures for Machine Learning: Challenges, Opportunities, and Research Roadmap
Hafiz Rehan
Javed Muhammad Usama
Abbas Sarmad
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Mrázek Vojtěch, Ing., Ph.D. (DCSY)
machine learning, approximate computing, deep learning, neural networks, energy
efficiency
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.
@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/"
}