Edge-Accelerated Deep Learning

边缘加速深度学习

• 批准号: DP200103718
• 负责人: A/Prof Bing Zhou
• 金额: $ 27.38万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200103718
• 关键词: Edge; Accelerated; Deep; Learning

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.

实现深度学习（DL）应用通常需要在云中收集大量数据和强大的计算资源。然而，这种集中式方法具有高延迟、大带宽使用以及许多实际应用中可能的隐私侵犯的问题。如果不妥善解决这些问题，深度学习在实践中的广泛应用将受到严重阻碍。该项目旨在解决在分布式边缘计算环境中有效部署和高效执行DL应用程序的几个关键挑战性问题。将开发几种创新的边缘计算方法用于DL训练，推理和实现，以实现低延迟和增强隐私的高性能。