196 citations · 1.1k across the 25 of their papers we have counts for
28 papers
AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines
Dimitrios Danopoulos, Enrico Lupi, Chang Sun +4
Efficient AI inference on AMD's Versal AI Engine (AIE) is challenging due to tightly coupled VLIW execution, explicit datapaths, and local memory management. Prior work focused on…
It's not a FAD: first results in using Flows for unsupervised Anomaly Detection at 40 MHz at the Large Hadron Collider
Francesco Vaselli, Chang Sun, Thea Aarrestad +7
We present the first implementation of a Continuous Normalizing Flow (CNF) model for unsupervised anomaly detection within the realistic, high-rate environment of the Large Hadron…
HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs
Chang Sun, Zhiqiang Que, Thea K. Årrestad +4
Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quanti…
Ultrafast jet classification on FPGAs for the HL-LHC
Patrick Odagiu, Zhiqiang Que, Javier Duarte +13
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, w…
FPGA Resource-aware Structured Pruning for Real-Time Neural Networks
Benjamin Ramhorst, Vladimir Loncar, George A. Constantinides
Neural networks achieve state-of-the-art performance in image classification, speech recognition, scientific analysis and many more application areas. Due to the high computational…
Symbolic Regression on FPGAs for Fast Machine Learning Inference
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar +7
The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitiv…
Tailor: Altering Skip Connections for Resource-Efficient Inference
Olivia Weng, Gabriel Marcano, Vladimir Loncar +7
Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and of…
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Elham E Khoda, Dylan Rankin, Rafael Teixeira de Lima +10
Recurrent neural networks have been shown to be effective architectures for many tasks in high energy physics, and thus have been widely adopted. Their use in low-latency environme…
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…
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini +8
In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Consi…
Lightweight Jet Reconstruction and Identification as an Object Detection Task
Adrian Alan Pol, Thea Aarrestad, Ekaterina Govorkova +8
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC…
Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +11
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely ch…
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
Zhiqiang Que, Erwei Wang, Umar Marikar +10
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational i…
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig +15
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off…
Jet Single Shot Detection
Adrian Alan Pol, Thea Aarrestad, Katya Govorkova +8
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on C…
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig +27
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.…
Fast convolutional neural networks on FPGAs with hls4ml
Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti +17
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate a…
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
Aneesh Heintz, Vesal Razavimaleki, Javier Duarte +18
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a fram…
HL-LHC Computing Review: Common Tools and Community Software
HEP Software Foundation, :, Thea Aarrestad +107
Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will b…
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta +19
Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering…
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Claudionor N. Coelho, Aki Kuusela, Shan Li +7
Although the quest for more accurate solutions is pushing deep learning research towards larger and more complex algorithms, edge devices demand efficient inference and therefore r…
Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
Giuseppe Di Guglielmo, Javier Duarte, Philip Harris +13
We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with FPG…
Fast inference of Boosted Decision Trees in FPGAs for particle physics
Sioni Summers, Giuseppe Di Guglielmo, Javier Duarte +10
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion p…
C and Fortran OpenMP programs for rotating Bose-Einstein condensates
Ramavarmaraja Kishor Kumar, Vladimir Loncar, Paulsamy Muruganandam +2
We present OpenMP versions of C and Fortran programs for solving the Gross-Pitaevskii equation for a rotating trapped Bose-Einstein condensate (BEC) in two (2D) and three (3D) spat…