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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Coupled VO2 Oscillators Circuit as Analog First Layer Filter in Convolutional Neural Networks
|
|---|---|
| Published in |
Frontiers in Neuroscience, February 2021
|
| DOI | 10.3389/fnins.2021.628254 |
| Pubmed ID | |
| Authors |
Elisabetta Corti, Joaquin Antonio Cornejo Jimenez, Kham M. Niang, John Robertson, Kirsten E. Moselund, Bernd Gotsmann, Adrian M. Ionescu, Siegfried Karg |
| Abstract |
In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area density and oscillation frequency. Further, the crossbar devices exhibit low variability and extended reliability, hence, enabling experiments on 4-coupled oscillator. We demonstrate the neuromorphic computing capabilities using the phase relation of the oscillators. As an application, we propose to replace digital filtering operation in a convolutional neural network with oscillating circuits. The concept is tested with a VGG13 architecture on the MNIST dataset, achieving performances of 95% in the recognition task. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 29% |
| Researcher | 3 | 7% |
| Student > Master | 3 | 7% |
| Other | 2 | 5% |
| Student > Doctoral Student | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 17 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 11 | 26% |
| Physics and Astronomy | 6 | 14% |
| Materials Science | 2 | 5% |
| Neuroscience | 2 | 5% |
| Unspecified | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 18 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 21 February 2021.
All research outputs
#2,968,377
of 25,387,668 outputs
Outputs from Frontiers in Neuroscience
#1,934
of 11,543 outputs
Outputs of similar age
#82,689
of 537,270 outputs
Outputs of similar age from Frontiers in Neuroscience
#126
of 404 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,543 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done well, scoring higher than 83% 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 537,270 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 404 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.