Speaker
Description
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.
| Talk's Q&A | During the talk |
|---|---|
| Talk duration | 15'+7' |
| Will you be able to present in person? | Yes |
| If we are unable to accommodate your oral presentation, would you be willing to consider presenting a poster instead? | Yes |