Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Impact of Device-Level Techniques on Energy-Efficiency of Neural
  Network Accelerators   Energy-efficiency is a key concern for neural network applications. To
alleviate this issue, hardware acceleration using FPGAs or GPUs can provide
better energy-efficiency than general-purpose processors. However, further
improvement of the energy-efficiency of such accelerators will be extremely
beneficial specially to deploy neural network in power-constrained edge
computing environments. In this paper, we experimentally explore the potential
of device-level energy-efficiency techniques (e.g.,supply voltage underscaling,
frequency scaling, and data quantization) for representative off-the-shelf
FPGAs compared to GPUs. Frequency scaling in both platforms can improve the
power and energy consumption but with performance overhead, e.g.,in GPUs it
improves the power consumption and GOPs/J by up to 34% and 28%, respectively.
However, leveraging reduced-precision instructions improves power (up to 13%),
energy (up to 20%), and performance (up to 7%) simultaneously, with negligible
reduction in accuracy of neural network accuracy.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys