Fast Machine Learning for Science Workshop 2023

Edge AI for accelerator controls: beam loss deblending

26 Sept 2023, 14:45

15m

Blackett Laboratory, Lecture Theatre 1 (Imperial College London)

Standard Talk Contributed Talks Contributed Talks

Speaker

Jovan Mitrevski (Fermi National Accelerator Lab. (US))

Description

In the Fermilab accelerator complex, the Main Injector (MI) and the Recycler Ring (RR) share a tunnel. The initial design was made for the needs of the Tevatron, where the RR stored fairly low intensities of anti-protons. Currently, however, both the MI and RR often have high intensity beams at the same time. Beam loss monitors (BLMs) are placed at different points in the tunnel to detect losses. However, it is often difficult to attribute the beam loss to either the MI or the RR, causing the beams to be unnecessarily aborted in both machines. This causes unnecessary downtime that is costly to the running of the experiments at the Intensity Frontier.

In order to decrease the unnecessary downtime, a system has to attribute the loss to a particular machine in real-time (less than 3 ms). To do this, a real-time AI system is used. A “central node” receives all the data from the BLMs and passes them to a U-Net-based AI model running on an Intel Arria10 SoC. This model attributes the likelihood that beam loss at a particular monitor is to a particular machine. This was the first FPGA-based edge AI control system running in the accelerator complex.

In addition to describing the system, a second focus of the presentation will be on using hls4ml to translate the original U-Net model, written in Keras, to High Level Synthesis (HLS) for the Intel HLS compiler. In particular, this model stressed the streaming implementation due to its design with skip connections, requiring some modification to the libraries. We will discuss our experience, and discuss plans for the future.

Presentation materials

READS FastML v3.pdf