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, 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 co
The increasing performance demands of AI models like Transformers for real-time applications such as jet tagging in particle physics are pushing the need for specialized, low-latency hardware solutions.
This research demonstrates a method for deploying complex AI models on resource-constrained edge devices, crucial for applications requiring immediate decision-making and efficient power consumption.
The ability to reconfigure hardware for specific AI tasks, particularly using integer-only quantized models, changes how high-performance computing at the edge can be achieved for complex AI workloads.
- · AMD
- · High-energy physics research
- · Edge AI hardware developers
- · FPGA/reconfigurable computing industry
- · Generic CPU/GPU solutions for low-latency edge AI
Increased adoption of reconfigurable computing architectures for specialized AI inferencing tasks.
Faster development and deployment cycles for AI models in scientific and industrial applications requiring real-time processing.
Enhanced capabilities for autonomous systems and real-time data analysis at the point of collection, reducing reliance on centralized cloud processing for critical functions.
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Read at arXiv cs.LG