Evaluation of Novel Fast Machine Learning Algorithms for Knowledge-Distillation-Based Anomaly Detection at CMS

Abstract

The CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) project aims to detect anomalous physics signatures without bias from theoretical models in proton-proton collisions at the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider. CICADA identifies anomalies in low-level calorimeter trigger data using a convolutional autoencoder, whose behavior is transferred to compact student models via knowledge distillation. Careful model design and quantization ensure sub-200 ns inference times on FPGAs. We investigate novel student model architectures that employ differentiable relaxations to enable extremely fast inference at the cost of slower training -- a welcome tradeoff in the knowledge distillation context. Evaluated on CMS open data and under emulated FPGA conditions, these models achieve comparable anomaly detection performance to classically quantized baselines with significantly reduced resource usage. The savings in resource usage enable the possibility to look at a richer input granularity.