# Anomaly detection with transformer models on AMD Versal

- Institution: CERN
- Speaker: Eliot Mario Abramo
- Year: 2026
- Language: English
- Source: [CERN](https://videos.cern.ch/record/3026661)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Teens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

**Project Description**:

This project targets the deployment of real-time anomaly detection algorithms on FPGA devices to identify physics phenomena that may evade standard, model-driven trigger selections in the CMS experiment. The central objective is the implementation of a ML architecture based on a transformer model, adapted for ultra-low-latency inference in hardware.

This project investigates whether modern heterogeneous hardware platforms, specifically AMD Adaptive Compute Acceleration Platforms (ACAPs), can meet our strict latency, throughput, and determinism requirements. These devices integrate programmable logic, processors, and Adaptive Intelligence (AI) Engines, offering a promising path toward scalable, real-time ML-based triggering and scouting solutions for CMS and future experiments.

**Supervisor**: Elias Leutgeb Thomas Owen James

00:00:00 Slide 1

00:00:06 Slide 2

00:00:26 Slide 3

00:01:01 Slide 4

00:01:30 Slide 5

00:02:06 Slide 6

00:02:53 Slide 7

00:03:12 Slide 8

00:03:27 Slide 9

00:04:18 Slide 10

00:04:38 Slide 11

00:05:01 Slide 12

00:05:30 Slide 13

## Lectures

1. [Anomaly detection with transformer models on AMD Versal](https://invisiblenetwork.ai/learn/cern/3026661/34d9407df98e), 6 min (CERN's player)