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

Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig, Manuel Blanco Valentin, Javier Duarte, Cristian Gingu, Philip Harris, James Hirschauer, Martin Kwok, Vladimir Loncar, Yingyi Luo, Llovizna Miranda, Jennifer Ngadiuba, Daniel Noonan, Seda Ogrenci-Memik, Maurizio Pierini, Sioni Summers, Nhan Tran*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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 implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. This is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.

Original languageEnglish (US)
Article number9447722
Pages (from-to)2179-2186
Number of pages8
JournalIEEE Transactions on Nuclear Science
Issue number8
StatePublished - Aug 2021


  • Application-specific integrated circuit (ASIC)
  • Large Hadron Collider (LHC)
  • artificial intelligence (AI)
  • autoencoder
  • hardware accelerator
  • high-level synthesis (HLS)
  • machine learning (ML)
  • single-event effect (SEE) mitigation

ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Nuclear Energy and Engineering
  • Electrical and Electronic Engineering


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