Framework to optimize DNN model inference on FPGA
Lead Research Organisation:
University of Glasgow
Department Name: School of Computing Science
Abstract
The idea in this proposal is to build a framework based on once-for-all network (OFA) trained networks to find out suitable ML models for a given FPGA architecture and user defined performance parameters.
Organisations
People |
ORCID iD |
| Jose Cano Reyes (Primary Supervisor) | |
| Rappy Saha (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/R513222/1 | 30/09/2018 | 29/09/2023 | |||
| 2749137 | Studentship | EP/R513222/1 | 02/10/2022 | 30/03/2026 | Rappy Saha |
| EP/T517896/1 | 30/09/2020 | 29/09/2025 | |||
| 2749137 | Studentship | EP/T517896/1 | 02/10/2022 | 30/03/2026 | Rappy Saha |
| EP/W524359/1 | 30/09/2022 | 29/09/2028 | |||
| 2749137 | Studentship | EP/W524359/1 | 02/10/2022 | 30/03/2026 | Rappy Saha |