Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Energy-Aware DNN Graph Optimization   Unlike existing work in deep neural network (DNN) graphs optimization for
inference performance, we explore DNN graph optimization for energy awareness
and savings for power- and resource-constrained machine learning devices. We
present a method that allows users to optimize energy consumption or balance
between energy and inference performance for DNN graphs. This method
efficiently searches through the space of equivalent graphs, and identifies a
graph and the corresponding algorithms that incur the least cost in execution.
We implement the method and evaluate it with multiple DNN models on a GPU-based
machine. Results show that our method achieves significant energy savings,
i.e., 24% with negligible performance impact.

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The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin