| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 01023 | |
| Number of page(s) | 6 | |
| Section | 1 - Online and Real-time Computing | |
| DOI | https://doi.org/10.1051/epjconf/202024501023 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024501023
Highly Performant, Deep Neural Networks with sub-microsecond latency on FPGAs for Trigger Applications
1
Institut für Physik, Johannes Gutenberg-Universität Mainz, Mainz; Germany
2
Cluster of Excellence PRISMA +, Johannes Gutenberg-Universität Mainz, Mainz; Germany
* e-mail: schmittc@uni-mainz.de
Published online: 16 November 2020
Artificial neural networks are becoming a standard tool for data analysis, but their potential remains yet to be widely used for hardware-level trigger applications. Nowadays, high-end FPGAs, often used in low-level hardware triggers, offer theoretically enough performance to include networks of considerable size. This makes it very promising and rewarding to optimize a neural network implementation for FPGAs in the trigger context.
Here an optimized neural network implementation framework is presented, which typically reaches 90 to 100% computational efficiency, requires few extra FPGA resources for data flow and controlling, and allows latencies in the order of 10s to few 100s of nanoseconds for entire (deep) networks.
© The Authors, published by EDP Sciences, 2020
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