| | |
| | |
Stat |
Members: 3643 Articles: 2'487'895 Articles rated: 2609
29 March 2024 |
|
| | | |
|
Article overview
| |
|
Resource-Efficient Neural Networks for Embedded Systems | Wolfgang Roth
; Günther Schindler
; Matthias Zöhrer
; Lukas Pfeifenberger
; Robert Peharz
; Sebastian Tschiatschek
; Holger Fröning
; Franz Pernkopf
; Zoubin Ghahramani
; | Date: |
7 Jan 2020 | Abstract: | While machine learning is traditionally a resource intensive task, embedded
systems, autonomous navigation, and the vision of the Internet of Things fuel
the interest in resource-efficient approaches. These approaches aim for a
carefully chosen trade-off between performance and resource consumption in
terms of computation and energy. The development of such approaches is among
the major challenges in current machine learning research and key to ensure a
smooth transition of machine learning technology from a scientific environment
with virtually unlimited computing resources into every day’s applications. In
this article, we provide an overview of the current state of the art of machine
learning techniques facilitating these real-world requirements. In particular,
we focus on deep neural networks (DNNs), the predominant machine learning
models of the past decade. We give a comprehensive overview of the vast
literature that can be mainly split into three non-mutually exclusive
categories: (i) quantized neural networks, (ii) network pruning, and (iii)
structural efficiency. These techniques can be applied during training or as
post-processing, and they are widely used to reduce the computational demands
in terms of memory footprint, inference speed, and energy efficiency. We
substantiate our discussion with experiments on well-known benchmark data sets
to showcase the difficulty of finding good trade-offs between
resource-efficiency and predictive performance. | Source: | arXiv, 2001.3048 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser claudebot
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |