AI Engines enable larger low-latency neural networks for extreme-edge scientific computing on FPGAs than programmable logic, via a new latency-adjusted resource equivalence metric and tailored optimizations.
Cat: Customized transformer accelerator framework on versal acap
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Presents a reusable open-source framework for mapping quantized transformer layers to AMD Versal AI Engine tiles for jet tagging at the LHC.
citing papers explorer
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Design Rules for Extreme-Edge Scientific Computing on AI Engines
AI Engines enable larger low-latency neural networks for extreme-edge scientific computing on FPGAs than programmable logic, via a new latency-adjusted resource equivalence metric and tailored optimizations.
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Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
Presents a reusable open-source framework for mapping quantized transformer layers to AMD Versal AI Engine tiles for jet tagging at the LHC.