Talks Archives - A3D3

Talks

Machine Learning Acceleration — Quantization Process and Tools Development

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

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

ACTS as a Service

Publications | Talks

FKeras: A Sensitivity Analysis Tool for Edge Neural Networks

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 | Talks

Parameter estimation using Likelihood-free Inference

Publications | Talks

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

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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

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

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

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