Deep Learning Optimiser
This use case develops a deep learning optimiser that uses OmpSs to reduce the energy consumption of deep learning inference significantly. The optimiser makes use of OmpSs to increase the computational architectures supported by the optimiser to CPU, GPU and FPGA. The focus and aim is to facilitate the deployment of deep learning based AI on embedded systems for users in an energy- and cost-efficient manner.
LEGaTO enabled Machine Intelligence Sweden, one of its partners, to develop a Deep Learning Optimiser that has been spun out as its own company, EmbeDL. Furthermore, OmpSs@FPGA lowers the entry barrier for FPGA-development and has thus greatly improved the efficiency of the FPGA-development cycle.
OmpSs@FPGA has been used to develop an FPGA implementation for deployment of deep neural networks.
LEGaTO components are available here: https://legato-project.eu/software/components
- The Connector AI Digital Conference, on 9 June 2020
- MASA Day Digital, on 15 May 2020
- SSII AI, Big Data & XR Networking Conference, on 26 September 2019
|LEGaTO website||April 2020||LV-EmbeDL: Transferring LEGaTO-developed Techniques to Industry|
|HiPEAC info||January 2020||Bright sparks - Tackling the energy challenge in computing systems|
|LEGaTO website||December 2019||LEGaTO partner Hans Salomonsson wins HiPEAC Technology Transfer Award|
|LEGaTO website||March 2019||Reducing energy while maintaining reliability: FPGAs and neural networks|
|LEGaTO website||February 2018||New project to plug the software-stack support gap for energy-efficient computing|