LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors

1 code implementation • 28 Sep 2022 • Zhiqiang Que, Hongxiang Fan, Marcus Loo, Michaela Blott, Maurizio Pierini, Alexander D Tapper, Wayne Luk

In addition, we introduce an outer-product based matrix multiplication approach which is enhanced by the strength reduction for low latency design.

Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator

no code implementations • 24 Nov 2021 • Hongxiang Fan, Martin Ferianc, Zhiqiang Que, He Li, Shuanglong Liu, Xinyu Niu, Wayne Luk

Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs.

Applications and Techniques for Fast Machine Learning in Science

no code implementations • 25 Oct 2021 • Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

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 experimental data processing loop to accelerate scientific discovery.

BIG-bench Machine Learning

Accelerating Recurrent Neural Networks for Gravitational Wave Experiments

1 code implementation • 26 Jun 2021 • Zhiqiang Que, Erwei Wang, Umar Marikar, Eric Moreno, Jennifer Ngadiuba, Hamza Javed, Bartłomiej Borzyszkowski, Thea Aarrestad, Vladimir Loncar, Sioni Summers, Maurizio Pierini, Peter Y Cheung, Wayne Luk

The proposed approach has been evaluated based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA.

Time Series

Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator

no code implementations • 4 Jun 2021 • Martin Ferianc, Zhiqiang Que, Hongxiang Fan, Wayne Luk, Miguel Rodrigues

To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs.

Time Series Time Series Analysis