Open Source Summit Korea 2026

Machine learning is central to high-energy physics,.These environments impose strict latency, throughput, and memory constraints, requiring data processing at unprecedented rates. Addressing these demands requires efficient, hardware-aware inference across heterogeneous architectures.

The ML4EP team at CERN is developing an integrated ecosystem for this purpose. We present recent advances in efficient ML deployment. First, aie4ml ports trained models to next-generation AMD FPGAs for ultra-low-latency inference. PQuantML complements this with hardware-aware pruning and quantization, reducing model size and compute cost while preserving performance.

For CPU and GPU inference, SOFIE translates trained models into optimized C++ for heterogeneous systems. Using alpaka, it enables backend-agnostic execution while minimizing data movement. It integrates with PQuantML to support quantized models and is deployable in both online systems and offline workflows.