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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automatic Optimization of the Computation Graph in the Nengo Neural Network Simulator
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, May 2017
|
| DOI | 10.3389/fninf.2017.00033 |
| Pubmed ID | |
| Authors |
Jan Gosmann, Chris Eliasmith |
| Abstract |
One critical factor limiting the size of neural cognitive models is the time required to simulate such models. To reduce simulation time, specialized hardware is often used. However, such hardware can be costly, not readily available, or require specialized software implementations that are difficult to maintain. Here, we present an algorithm that optimizes the computational graph of the Nengo neural network simulator, allowing simulations to run more quickly on commodity hardware. This is achieved by merging identical operations into single operations and restructuring the accessed data in larger blocks of sequential memory. In this way, a time speed-up of up to 6.8 is obtained. While this does not beat the specialized OpenCL implementation of Nengo, this optimization is available on any platform that can run Python. In contrast, the OpenCL implementation supports fewer platforms and can be difficult to install. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Switzerland | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 20 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 25% |
| Student > Master | 4 | 20% |
| Researcher | 2 | 10% |
| Student > Postgraduate | 2 | 10% |
| Other | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 3 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 25% |
| Engineering | 5 | 25% |
| Neuroscience | 4 | 20% |
| Psychology | 1 | 5% |
| Materials Science | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 3 | 15% |
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 08 September 2021.
All research outputs
#7,346,143
of 24,226,848 outputs
Outputs from Frontiers in Neuroinformatics
#345
of 795 outputs
Outputs of similar age
#109,589
of 314,928 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#8
of 17 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 795 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has gotten more attention than average, scoring higher than 56% 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 314,928 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 17 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 58% of its contemporaries.