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Grant Award View - GA68332-V1
Edge-Accelerated Deep Learning
GA ID:
GA68332-V1
Agency:
Australian Research Council
Approval Date:
19-Dec-2019
Variation Publish Date:
2-Aug-2021
Variation Date:
27-Jul-2021
Category:
Science, Technology, Engineering and Mathematics (STEM) Research
Grant Term:
1-Jan-2020 to 31-Dec-2022
Value (AUD):
$429,000.00
(GST inclusive where applicable)
Varies:
GA68332
- Edge-Accelerated Deep Learning
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 19/20 Discovery
Grant Program:
Discovery Projects
Grant Activity:
Edge-Accelerated Deep Learning
Purpose:
Implementing deep learning (DL) applications usually requires a large amount of collected data and powerful computing resources in the cloud. However, this centralised approach has issues of high latency, large bandwidth usage, and possible privacy violation for many practical applications. Without properly addressing these issues, the wider application of DL in practice will seriously be hindered. This project aims to solve several key challenging problems in effective deployment and efficient execution of DL applications in a distributed edge-computing environment. Several innovative edge-computing methods will be developed for DL training, inference and implementation to achieve high performance with low latency and enhanced privacy.
GO ID:
GO Title:
Discovery Projects for funding commencing in 2020
Internal Reference ID:
DP20 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
The University of Sydney
Recipient ABN:
15 211 513 464
Grant Recipient Location
Suburb:
FOREST LODGE
Town/City:
FOREST LODGE
Postcode:
2037
State/Territory:
NSW
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
NSW
Postcode:
2037
Country:
AUSTRALIA