CICADA Calorimeter Image Convolutional Anomaly Detection Algorithm (CICADA) uses low level Compact Muon Solenoid experiment’s trigger calorimeter information as the input to convolutional autoencoder to find anomalies produced during Large Hadron Collider proton-proton collisionsis. Quantization Aware Training and Knowledge Distillation are used to compress the model for sub-500ns inference on Field-Programmable Gate Arrays.