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Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-Quantized Log-Gradient Input Images

Overview of attention for article published in ACM Transactions on Embedded Computing Systems, April 2023
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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5 X users

Citations

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2 Dimensions

Readers on

mendeley
9 Mendeley
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Title
Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-Quantized Log-Gradient Input Images
Published in
ACM Transactions on Embedded Computing Systems, April 2023
DOI 10.1145/3591466
Authors

Qianyun Lu, Boris Murmann

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 11%
Unknown 8 89%
Readers by discipline Count As %
Engineering 1 11%
Unknown 8 89%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 April 2023.
All research outputs
#8,583,215
of 25,753,031 outputs
Outputs from ACM Transactions on Embedded Computing Systems
#90
of 370 outputs
Outputs of similar age
#148,288
of 423,794 outputs
Outputs of similar age from ACM Transactions on Embedded Computing Systems
#3
of 9 outputs
Altmetric has tracked 25,753,031 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 370 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 423,794 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.