Speaker
Description
SOFIE (System for Optimized Fast Inference code Emit), being developed by the ML4EP Project at CERN, translates trained machine learning models into self-contained, low-latency C++ code that is portable, hardware-agnostic, and highly optimized while depending only on BLAS libraries.
SOFIE achieves portability across heterogeneous computing architectures by leveraging the abstract buffer definitions provided by the alpaka[1] library. The generated code incorporates several inference optimizations, including kernel fusion, efficient memory usage, and support for quantized models, enabling high-performance inference on modern CPU and accelerator platforms.
Beyond standalone inference, SOFIE serves as a component in several projects. It powers Yukti, a header-only interface that provides a unified zero-copy API for machine learning inference runtimes; it is integrated with RooFit as a neural surrogate for likelihood evaluation, with automatic differentiation provided by CLAD; and it could embed a hardware-agnostic, optimized compression and decompression pipeline for BOA Constrictor[2].
In this work, we present comprehensive benchmarking and profiling results for machine learning models used in high-energy physics, including ParticleNet, ATLAS GN2, State Space Models, ParticleFlow networks, and other jet-tagging architectures. We evaluate inference latency, throughput, memory footprint, and scalability across different hardware backends, highlighting the impact of SOFIE's code generation and optimization strategies. These results demonstrate SOFIE's ability to provide portable, efficient, and high-performance inference for modern machine learning workloads in jet reconstruction, identification and other performance sensitive environments having constraints on latency and memory.
[1] Matthes, A., Widera, R., Zenker, E., Worpitz, B., Huebl, A., & Bussmann, M. (2017, June 30). Tuning and optimization for a variety of many-core architectures without changing a single line of implementation code using the Alpaka library. Retrieved from http://arxiv.org/abs/1706.10086
[2] Gupta, A., Doglioni, C., & Elliott, T. J. (2025). BOA Constrictor: A Mamba-based lossless compressor for High Energy Physics data. arXiv [Physics.Comp-Ph]. Retrieved from http://arxiv.org/abs/2511.11337