A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

• Published in: IEEE transactions on nuclear science Vol. 68; no. 8; pp. 2179 - 2186
• Main Authors: Guglielmo, Giuseppe Di, Fahim, Farah, Herwig, Christian, Valentin, Manuel Blanco, Duarte, Javier, Gingu, Cristian, Harris, Philip, Hirschauer, James, Kwok, Martin, Loncar, Vladimir, Luo, Yingyi, Miranda, Llovizna, Ngadiuba, Jennifer, Noonan, Daniel, Ogrenci-Memik, Seda, Pierini, Maurizio, Summers, Sioni, Tran, Nhan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acidity; Algorithms; Application specific integrated circuits; application-specific integrate circuit (ASIC); Application-specific integrated circuit (ASIC); artificial intelligence (AI); Artificial neural networks; autoencoder; CMOS; Compression; Computer architecture; Data compression; Data transmission; Design optimization; Detectors; Energy consumption; Field programmable gate arrays; Gates (circuits); hardware accelerator; High level synthesis; high-level synthesis (HLS); INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Integrated circuits; Ionizing radiation; Large Hadron Collider; Large Hadron Collider (LHC); Learning algorithms; Machine learning; machine learning (ML); Neural networks; Particle physics; PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Power consumption; Radiation; Sensors; single-event effect (SEE) mitigation; Sodium channels; Solenoids; Task analysis; Training

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be imp...