A Case For Adaptive Deep Neural Networks in Edge Computing
- URL: http://arxiv.org/abs/2008.01814v2
- Date: Wed, 16 Dec 2020 14:27:36 GMT
- Title: A Case For Adaptive Deep Neural Networks in Edge Computing
- Authors: Francis McNamee and Schahram Dustadar and Peter Kilpatrick and Weisong
Shi and Ivor Spence and Blesson Varghese
- Abstract summary: This paper investigates whether there is a case for adaptive Deep Neural Networks (DNNs) in edge computing.
The results show that network conditions affects DNN performance more than CPU or memory related operational conditions.
- Score: 1.683310745678261
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Edge computing offers an additional layer of compute infrastructure closer to
the data source before raw data from privacy-sensitive and performance-critical
applications is transferred to a cloud data center. Deep Neural Networks (DNNs)
are one class of applications that are reported to benefit from collaboratively
computing between the edge and the cloud. A DNN is partitioned such that
specific layers of the DNN are deployed onto the edge and the cloud to meet
performance and privacy objectives. However, there is limited understanding of:
(a) whether and how evolving operational conditions (increased CPU and memory
utilization at the edge or reduced data transfer rates between the edge and the
cloud) affect the performance of already deployed DNNs, and (b) whether a new
partition configuration is required to maximize performance. A DNN that adapts
to changing operational conditions is referred to as an 'adaptive DNN'. This
paper investigates whether there is a case for adaptive DNNs in edge computing
by considering three questions: (i) Are DNNs sensitive to operational
conditions? (ii) How sensitive are DNNs to operational conditions? (iii) Do
individual or a combination of operational conditions equally affect DNNs? (iv)
Is DNN partitioning sensitive to hardware architectures on the cloud/edge? The
exploration is carried out in the context of 8 pre-trained DNN models and the
results presented are from analyzing nearly 8 million data points. The results
highlight that network conditions affects DNN performance more than CPU or
memory related operational conditions. Repartitioning is noted to provide a
performance gain in a number of cases, but a specific trend was not noted in
relation to its correlation to the underlying hardware architecture.
Nonetheless, the need for adaptive DNNs is confirmed.
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