Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
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Computer Science > Machine Learning
Title:Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
Abstract:Transformer-based models achieve strong performance for jet tagging at the CERN LHC, but deploying them in low-latency, resource-constrained trigger systems is challenging. We present an initial implementation of a quantized, integer-only transformer for jet tagging on the AMD Versal AI Engine (AIE), mapping dense and multi-head attention (MHA) layers to AIE tiles. The main contribution is a reusable software framework that represents transformer layers as composable AIE building blocks and automatically generates the corresponding Vitis graph code from a high-level Python model description. This framework provides a foundation for future research and is released as open-source software at this https URL.
| Comments: | 4 pages, 4 figures. In FCCM 2026 proceedings |
| Subjects: | Machine Learning (cs.LG); Hardware Architecture (cs.AR) |
| Cite as: | arXiv:2606.17500 [cs.LG] |
| (or arXiv:2606.17500v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17500
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | 2026 IEEE 34th Int. Symp. on Field-Programmable Custom Computing Machines (FCCM), Atlanta, GA, USA, 2026, pp. 307-310 |
| Related DOI: | https://doi.org/10.1109/FCCM68464.2026.00078
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