# KalEdge-Lite: Hardware-Aware ML-to-FPGA Deployment with Automated hls4ml Integration

- Institution: CERN
- Speaker: Romina Soledad Molina
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3025770)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

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.

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## Lectures

1. [KalEdge-Lite: Hardware-Aware ML-to-FPGA Deployment with Automated hls4ml Integration](https://invisiblenetwork.ai/learn/cern/3025770/7fff984dba1f), 25 min (CERN's player)