Publications Archives - A3D3

Publications

Machine Learning Acceleration — Quantization Process and Tools Development

Machine Learning acceleration in the Global Event Processor of the ATLAS Trigger Update

Accelerating CNNs on FPGAs for Particle Energy Reconstruction

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml

LHC physicists can’t save them all

Portable Acceleration of CMS Mini-AOD Production with Coprocessors as a Service

Jets as sets or graphs: Fast jet classification on FPGAs for efficient triggering at the HL-LHC

MLPF: Machine Learning for Particle Flow

Publications | Talks

Scalable neural network models and terascale datasets for particle-flow reconstruction

Publications | Papers

Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors

ACTS as a Service

Publications | Talks

FKeras: A Sensitivity Analysis Tool for Edge Neural Networks

Publications | Papers

ScaleHLS, a Scalable High-level Synthesis Framework with Multi-level Transformations and Optimizations

Publications | Papers

AutoScaleDSE: A Scalable Design Space Exploration Engine for High-Level Synthesis

Publications | Talks

Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs

Publications | Talks

FPGA Deployment of LFADS for Real-time Neuroscience Experiments

Publications | Talks

Quantifying the Efficiency of High-Level Synthesis for Machine Learning Inference

Publications | Talks

Fast ML in the NSF HDR Institute: A3D3

Publications | Talks

Community Vision, Needs, and Progress

Publications | Talks

ZTF SCoPe: A Catalog of Variable Sources

Publications | Papers

GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders

Publications | Talks

Parameter estimation using Likelihood-free Inference

Publications | Talks

BEVFusion-R: Efficient and Deployment-Ready Camera-Radar Fusion

Publications | Talks

SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer

Publications | Talks

FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer

Publications | Talks

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation

Publications | Papers

SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning

Publications | Papers

Structural Re-weighting Improves Graph Domain Adaptation

Publications | Papers

Progress towards an improved particle flow algorithm at CMS with machine learning, 2023, ACAT

Semi-supervised Graph Neural Networks for Pileup Noise Removal, 2022, NeurIPS AI4Science, EPJC

Publications | Papers

Interpretable Geometric Deep Learning via Learnable Randomness Injection, 2023, ICLR

Neighborhood-aware Scalable Temporal Network Representation Learning, 2022, LoG

Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning, 2022, VLDB

Publications | Papers

TorchSparse++: Efficient Point Cloud Engine

NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers, 2023, submitted

Ye et al., Tutorials on ScaleHSL, FPGA, Feb. 27, 2022

Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism, 2022, ICML

Javier Duarte, Artificial Intelligence at the Edge of Particle Physics, HEP Seminar, Columbia University, November 17, 2021

Applications and Techniques for Fast Machine Learning in Science, 2021, Submitted to Front. Big Data

PointAcc: Efficient Point Cloud Accelerator, 2021, MICRO

Shih-Chieh Hsu, NSF HDR Institute: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery (A3D3), Fast ML General Meeting, October 1, 2021

ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation, 2022, HPCA

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution, 2020, ECCV

Point-Voxel CNN for Efficient 3D Deep Learning, 2019, NeurIPS (Spotlight)