About
KalEdge-Lite is a hardware-aware framework that automates neural network deployment on FPGA platforms through an end-to-end ML-to-bitstream workflow. The system integrates model training, compression techniques, hardware-aware evaluation, and automated accelerator generation. A regime-aware analytical model estimates FPGA resource utilization and latency before synthesis, enabling rapid identification of hardware-feasible configurations without repeated HLS execution. KalEdge-Lite supports multiple FPGA platforms and enables reproducible deployment from trained models to validated hardware implementations. The presentation will demonstrate practical deployment workflows and discuss experience using automated hardware-aware configuration screening for FPGA-based neural network acceleration.
00:00:00 Slide 1
00:00:13 Slide 2
00:02:24 Slide 3
00:07:43 Slide 4
00:12:58 Slide 5
00:13:14 Slide 6
00:13:24 Slide 7
00:14:18 Slide 8
00:14:50 Slide 9
00:15:43 Slide 10
00:16:05 Slide 11
The talk
Credits and licence
- Institution
- CERN
- Speaker
- Romina Soledad Molina
- Year
- 2026
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed