Fast inference of deep neural networks in FPGAs for particle physics

By Javier Mauricio Duarte, Song Han, Philip C. Harris, S. Jindariani, E. Kreinar, B. Kreis, J. Ngadiuba, M. Pierini, R. Rivera, Nhan Tran and 1 more. Journal of Instrumentation, 2018

520 citations recorded by the source corpus.

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very powerful in LHC physics, and particle physics as a whole. However, exploration of the use of such techniques in low-latency, low-power FPGA (Field Programmable Gate Array) hardware has only just begun. FPGA-based trigger and data acquisition systems have extremely low, sub-microsecond latency requirements that are unique to particle physics. We present a case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson. While we focus on a specific example, the lessons are far-reaching. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS increases accessibility across a broad user community and allows for a drastic decrease in firmware development time. We map out FPGA resource usage and latency versus neural network hyperparameters to identify the problems in particle physics that would benefit from performing neural network inference with FPGAs. For our example jet substructure model, we fit well within the available resources of modern FPGAs with a latency on the scale of 100 ns.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5e963ffdf331f003dfb25a598fc93a0deef1f728

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Citations in the corpus

Survey of Attacks and Defenses on Edge-Deployed Neural Networks (2019, methodology): “Storing that data is difficult, so authors of [18] propose to process the data in-flight using extremely lowlatency FPGA designs.”
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, background): “Similarly, data rates and processing speeds required in certain applications, such as particle physics experiments [133] or in wireless communications [134] can make online inference extremely challenging.”
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale (2019, methodology): “triggering [1] and event reconstruction by conditioning on potentially interesting events (e.g., q(ParticleType|·)≥ϵ). Recent activity exploring the use of FPGAs for NN inference for particle physics [23] will help implementation of these approaches, and HPC systems will be crucial in the training a”