9 citations · 15 across the 12 of their papers we have counts for
13 papers
On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
Daniel Abadjiev, Eliza Howard, Tsz Ngong You +35
A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Mu…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
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…
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes +31
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection…
Robustness of the \textit{smartpixels} classifier for different simulated sensor geometries and non-ideal detector conditions
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…
Characterization of a 28 nm ASIC With On-Chip ML for Particle Tracking Detectors
Benjamin Parpillon, Anthony Badea, Danush Shekar +35
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.…
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…
TimeFloats: Train-in-Memory with Time-Domain Floating-Point Scalar Products
Maeesha Binte Hashem, Benjamin Parpillon, Divake Kumar +2
In this work, we propose "TimeFloats," an efficient train-in-memory architecture that performs 8-bit floating-point scalar product operations in the time domain. While building on…
Skipper-in-CMOS: Non-Destructive Readout with Sub-Electron Noise Performance for Pixel Detectors
Agustin J. Lapi, Miguel Sofo-Haro, Benjamin C. Parpillon +15
The Skipper-in-CMOS image sensor integrates the non-destructive readout capability of Skipper Charge Coupled Devices (Skipper-CCDs) with the high conversion gain of a pinned photod…
Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties
Jennet Dickinson, Rachel Kovach-Fuentes, Lindsey Gray +20
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-g…
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Jieun Yoo, Jennet Dickinson, Morris Swartz +19
Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced,…
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
Shruti R. Kulkarni, Aaron Young, Prasanna Date +12
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experim…
Memory-Immersed Collaborative Digitization for Area-Efficient Compute-in-Memory Deep Learning
Shamma Nasrin, Maeesha Binte Hashem, Nastaran Darabi +4
This work discusses memory-immersed collaborative digitization among compute-in-memory (CiM) arrays to minimize the area overheads of a conventional analog-to-digital converter (AD…
Smart sensors using artificial intelligence for on-detector electronics and ASICs
Gabriella Carini, Grzegorz Deptuch, Jennet Dickinson +19
Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noi…