Task 1.3: Hardware-aware AI optimization

Task leads: Maurizio Pierini
This task focuses on development of software infrastructure for training efficient neural networks that will provide a standardized interface unifying neural network compression and optimization techniques such as pruning, quantization and neural architecture search. This work contributes to existing international projects; it will enable the development and deployment of hardware-optimal AI-based real-time algorithms at CERN, as described in WP2 and WP3.
Publications and other resources
- Roope Niemi, presentation at FastML, 02/09/2025, “End-to-End Neural Network Compression and Deployment for Hardware Acceleration Using PQuant and hls4ml”
- Roope Niemi, presentation at ACAT 09/09/2025, “End-to-end hardware-aware model compression and deployment with PQuant and hls4ml”
