77 citations · 113 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator
Martin Ferianc, Zhiqiang Que, Hongxiang Fan +2
Neural networks have demonstrated their outstanding performance in a wide range of tasks. Specifically recurrent architectures based on long-short term memory (LSTM) cells have man…
An Analysis of Alternating Direction Method of Multipliers for Feed-forward Neural Networks
Seyedeh Niusha Alavi Foumani, Ce Guo, Wayne Luk
In this work, we present a hardware compatible neural network training algorithm in which we used alternating direction method of multipliers (ADMM) and iterative least-square meth…
An FPGA Accelerated Method for Training Feed-forward Neural Networks Using Alternating Direction Method of Multipliers and LSMR
Seyedeh Niusha Alavi Foumani, Ce Guo, Wayne Luk
In this project, we have successfully designed, implemented, deployed and tested a novel FPGA accelerated algorithm for neural network training. The algorithm itself was developed…