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Transformer models optimized for CERN jet tagging on AMD Versal AI Engine

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for deploying transformer models on the AMD Versal AI Engine, specifically for jet tagging applications at the CERN LHC. This framework quantifies transformer layers into an integer-only format, making them suitable for low-latency, resource-constrained trigger systems. The reusable software allows for the automatic generation of Vitis graph code from high-level Python descriptions, offering a foundation for future research in this area. AI

RANK_REASON The cluster contains an academic paper detailing a novel implementation of AI models on specialized hardware for a scientific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Gram Koski, Sean Lipps, Zhenghua Ma, G. Abarajithan, Ryan Kastner ·

    Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines

    arXiv:2606.17500v1 Announce Type: new 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, intege…

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