  Our objective will be to integrate ML into Fermilab accelerator operations
and furthermore provide an accessible framework which can also be used by a
broad range of other accelerator systems with dynamic tuning needs. We will
develop of real-time accelerator control using embedded ML on-chip hardware and
fast communication between distributed systems in this proposal. We will
demonstrate this technology for the Mu2e experiment by increasing the overall
duty factor and uptime of the experiment through two synergistic projects.
First, we will use deep reinforcement learning techniques to improve the
performance of the regulation loop through guided optimization to provide
stable proton beams extracted from the Delivery Ring to the Mu2e experiment.
This requires the development of a digital twin of the system to model the
accelerator and develop real-time ML algorithms. Second, we will use
de-blending techniques to disentangle and classify overlapping beam losses in
the Main Injector and Recycler Ring to reduce overall beam downtime in each
machine. This ML model will be deployed within a semi-autonomous operational
mode. Both applications require processing at the millisecond scale and will
share similar ML-in-hardware techniques and beam instrumentation readout
technology. A collaboration between Fermilab and Northwestern University will
pull together the talents and resources of accelerator physicists, beam
instrumentation engineers, embedded system architects, FPGA board design
experts, and ML experts to solve complex real-time accelerator controls
challenges which will enhance the physics program. More broadly, the framework
developed for Accelerator Real-time Edge AI Distributed Systems (READS) can be
applied to future projects as the accelerator complex is upgraded for the
PIP-II and DUNE era.
