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20212023
most citedApplications and Techniques for Fast Machine Learning in Science

77 citations · 134 across the 12 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

quant-ph2022★ 6 cited

Neural network accelerator for quantum control

David Xu, A. Barış Özgüler, Giuseppe Di Guglielmo +4

Efficient quantum control is necessary for practical quantum computing implementations with current technologies. Conventional algorithms for determining optimal control parameters…

cs.LG2022★ 5 cited

FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

Javier Duarte, Nhan Tran, Ben Hawks +4

Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is thei…

cs.LG2022★ 9 cited

QONNX: Representing Arbitrary-Precision Quantized Neural Networks

Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott +11

We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce s…

cs.LG2022★ 5 cited

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

Hendrik Borras, Giuseppe Di Guglielmo, Javier Duarte +15

We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml an…

physics.ins-det2022★ 6 cited

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…

cs.LG2022

Physics Community Needs, Tools, and Resources for Machine Learning

Philip Harris, Erik Katsavounidis, William Patrick McCormack +18

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this w…

hep-ex2022★ 10 cited

Semi-supervised Graph Neural Networks for Pileup Noise Removal

Tianchun Li, Shikun Liu, Yongbin Feng +4

The high instantaneous luminosity of the CERN Large Hadron Collider leads to multiple proton-proton interactions in the same or nearby bunch crossings (pileup). Advanced pileup mit…