4 papers
Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
Gram Koski, Sean Lipps, Zhenghua Ma +2
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 pres…
Design Rules for Extreme-Edge Scientific Computing on AI Engines
Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos +3
Extreme-edge scientific applications use machine learning models to analyze sensor data and make real-time decisions. Their stringent latency and throughput requirements demand sma…
FireBridge: Cycle-Accurate Hardware + Firmware Co-Verification for Modern Accelerators
G Abarajithan, Zhenghua Ma, Francesco Restuccia +1
Hardware-firmware integration is becoming a productivity bottleneck due to the increasing complexity of accelerators, characterized by intricate memory hierarchies and firmware-int…
CGRA4ML: A Hardware/Software Framework to Implement Neural Networks for Scientific Edge Computing
G Abarajithan, Zhenghua Ma, Ravidu Munasinghe +2
The scientific community increasingly relies on machine learning (ML) for near-sensor processing, leveraging its strengths in tasks such as pattern recognition, anomaly detection,…