Search Results for author: Zhiqiang Que

Found 8 papers, 3 papers with code

When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA

2 code implementations13 Aug 2023 Hongxiang Fan, Hao Chen, Liam Castelli, Zhiqiang Que, He Li, Kenneth Long, Wayne Luk

Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving.

Autonomous Driving

MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration

no code implementations14 Jun 2023 Zhiqiang Que, Shuo Liu, Markus Rognlien, Ce Guo, Jose G. F. Coutinho, Wayne Luk

This paper introduces a novel optimization framework for deep neural network (DNN) hardware accelerators, enabling the rapid development of customized and automated design flows.

LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics

1 code implementation28 Sep 2022 Zhiqiang Que, Hongxiang Fan, Marcus Loo, He Li, Michaela Blott, Maurizio Pierini, Alexander Tapper, Wayne Luk

This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance.

Graph Neural Network

Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator

no code implementations24 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 implementations25 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

Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator

no code implementations4 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 Analysis

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