GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 206151_80748
- Type
- D - Journal article
- DOI
-
10.3389/fnins.2018.00941
- Title of journal
- Frontiers in Neuroscience
- Article number
- -
- First page
- 1
- Volume
- 12
- Issue
- 941
- ISSN
- 1662-453X
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2018
- URL
-
http://dx.doi.org/10.3389/fnins.2018.00941
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "The paper had significant impacts on the work of the SpiNNaker group on Manchester who have now followed our lead for on-device model building and have in response to this paper optimised their software stack to run the micro-circuit model in real time [1] (contact: Dr Oliver Rhodes, oliver.rhodes@manchester.ac.uk). It has also enabled a strong follow-on publication in Nature Computational Science [2].
[1] https://doi.org/10.1098/rsta.2019.0160
[2] https://doi.org/10.1038/s43588-020-00022-7"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
