77 citations · 134 across the 12 of their papers we have counts for
8 papers · 1 filter
Structural Re-weighting Improves Graph Domain Adaptation
Shikun Liu, Tianchun Li, Yongbin Feng +4
In many real-world applications, graph-structured data used for training and testing have differences in distribution, such as in high energy physics (HEP) where simulation data us…
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
Javier Campos, Zhen Dong, Javier Duarte +4
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specif…
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…
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…
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 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…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
MLPerf Tiny Benchmark
Colby Banbury, Vijay Janapa Reddi, Peter Torelli +19
Advancements in ultra-low-power tiny machine learning (TinyML) systems promise to unlock an entirely new class of smart applications. However, continued progress is limited by the…