6 papers · 1 filter
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes +33
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jenni Ott, Shiva Abbaszadeh +118
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Characterization of a 28 nm ASIC With On-Chip ML for Particle Tracking Detectors
Benjamin Parpillon, Anthony Badea, Danush Shekar +39
We present a 28 nm CMOS pixel readout integrated circuit implementing in-pixel analog signal processing and on-chip machine learning data filtering for particle tracking detectors.…
Sensor Co-design for
Danush Shekar, Ben Weiss, Morris Swartz +40
Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity…
Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS
Anthony Badea, Alice Bean, Doug Berry +16
Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly g…
Smart Pixels: In-pixel AI for on-sensor data filtering
Benjamin Parpillon, Chinar Syal, Jieun Yoo +16
We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine…