10 citations · 20 across the 12 of their papers we have counts for
14 papers
PQuantML: A Tool for End-to-End Hardware-aware Model Compression
Roope Niemi, Anastasiia Petrovych, Arghya Ranjan Das +9
PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to enviro…
Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml
Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar +5
This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physic…
SparsePixels: Efficient Convolution for Sparse Data on FPGAs
Ho Fung Tsoi, Dylan Rankin, Vladimir Loncar +1
Inference of standard convolutional neural networks (CNNs) on FPGAs often incurs high latency and a long initiation interval due to the deep nested loops required to densely convol…
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Jan-Frederik Schulte, Benjamin Ramhorst, Chang Sun +50
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can b…
wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
Benjamin Hawks, Jason Weitz, Dmitri Demler +13
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantl…
da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
Chang Sun, Zhiqiang Que, Vladimir Loncar +2
Neural networks with a latency requirement on the order of microseconds, like the ones used at the CERN Large Hadron Collider, are typically deployed on FPGAs fully unrolled and pi…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb
Fotis I. Giasemis, Vladimir Lončar, Bertrand Granado +1
In high-energy physics, the increasing luminosity and detector granularity at the Large Hadron Collider are driving the need for more efficient data processing solutions. Machine L…
Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors
J. Kvapil, G. Borca-Tasciuc, H. Bossi +30
This R\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiment…
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +6
We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously…
Low Latency Transformer Inference on FPGAs for Physics Applications with hls4ml
Zhixing Jiang, Dennis Yin, Yihui Chen +7
This study presents an efficient implementation of transformer architectures in Field-Programmable Gate Arrays(FPGAs) using hls4ml. We demonstrate the strategy for implementing the…
Reliable edge machine learning hardware for scientific applications
Tommaso Baldi, Javier Campos, Ben Hawks +15
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementatio…
Ultra Fast Transformers on FPGAs for Particle Physics Experiments
Zhixing Jiang, Dennis Yin, Elham E Khoda +6
This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool. Given the demons…
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu +1
Compact symbolic expressions have been shown to be more efficient than neural network models in terms of resource consumption and inference speed when implemented on custom hardwar…