
This research paper compares two high-level synthesis frameworks, SNL and hls4ml, for implementing machine learning algorithms on FPGAs for real-time anomaly detection in collider trigger systems, finding that while hls4ml excels in latency optimization, SNL offers greater resource efficiency, particularly for larger networks.


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analysis-of-slac-neural-network-library-snl-and-hls4ml-for-implementing-machine-learning-in-collider-trigger-systems


Analysis of SLAC Neural Network Library (SNL) and hls4ml for Implementing Machine Learning in Collider Trigger Systems
