Akademik Veri Yönetim Sistemi
TÜBİTAK - AB COST Projesi , 2025 - 2029
Deep Learning applications are becoming ubiquitous in science. Particle
physicists in particular are relying more and more on neural networks
for crucial tasks in their data processing. Often, they are confronted
with unique technological challenges, such as the need to operate deep
neural networks in real time and in extreme computing environments, such
as the event filtering systems of the experiments at the Large Hadron
Collider. Low latency and reduced computing resources imply strong
constraints for the algorithms operated on-edge, i.e., as close to the
detector as possible. To take advantage of the power of deep
learning under these conditions, one needs to develop compression
techniques and make the networks efficient without losing their
expressivity. This COST action aims at gathering researchers across
Europe interested in this challenging problem, to advance AI-powered
on-edge inference for next-generation particle physics experiments like
the High Luminosity LHC and future colliders. Through the technological
advancements envisioned, many other fields and sectors relying on fast
AI-based decision making and on-edge computation such as smartphones,
automotive, portable medical devices, drones, or satellites may too
profit from our initiative on Edge deeP learnIng foR pArticle PHYsics,
EPIGRAPHY.