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 https://github.com/KastnerRG/particle_transformer_aie.

翻译：基于Transformer的模型在欧洲核子研究中心大型强子对撞机上实现了优异的喷注标记性能，但在低延迟、资源受限的触发系统中部署这些模型仍具挑战性。我们提出了一个在AMD Versal AI引擎（AIE）上实现量化整型Transformer进行喷注标记的初始方案，将稠密层和多头注意力层映射至AIE瓦片。主要贡献在于开发了一套可重用的软件框架，将Transformer层表示为可复合的AIE构建模块，并从高层Python模型描述自动生成对应的Vitis图代码。该框架为未来研究奠定了坚实基础，并以开源软件形式发布于https://github.com/KastnerRG/particle_transformer_aie。