High-Performance FPGA-based Accelerator for Bayesian Neural Networks
Abstract
Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA-based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space.
Cite
@article{arxiv.2105.09163,
title = {High-Performance FPGA-based Accelerator for Bayesian Neural Networks},
author = {Hongxiang Fan and Martin Ferianc and Miguel Rodrigues and Hongyu Zhou and Xinyu Niu and Wayne Luk},
journal= {arXiv preprint arXiv:2105.09163},
year = {2021}
}
Comments
Design Automation Conference (DAC) 2021