Energy-Efficient Acceleration of Deep Neural Networks on Realtime-Constrained Embedded Edge Devices

Energy-Efficient Acceleration of Deep Neural Networks on Realtime-Constrained Embedded Edge Devices

This paper presents a hardware management technique that enables energy-efficient acceleration of deep neural networks (DNNs) on realtime-constrained embedded edge devices. It becomes increasingly common for edge devices to incorporate dedicated hardware accelerators for neural processing. The execu...

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Bibliographic Details
Main Authors: Bogil Kim, Sungjae Lee, Amit Ranjan Trivedi, William J. Song
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Deep neural networks
heterogeneous computing
embedded processors
hardware management
hardware measurement
energy efficiency
Online Access:https://ieeexplore.ieee.org/document/9262933/

Internet

https://ieeexplore.ieee.org/document/9262933/

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