High-Performance FPGA-based Accelerator for Bayesian Neural Networks

Hongxiang Fan; Martin Ferianc; Miguel Rodrigues; Hongyu Zhou; Xinyu Niu; Wayne Luk

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arXiv · 2105.09163

High-Performance FPGA-based Accelerator for Bayesian Neural Networks

Hongxiang Fan·Martin Ferianc·Miguel Rodrigues·Hongyu Zhou·Xinyu Niu·Wayne Luk

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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.

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BibTeX RIS

Hongxiang Fan, Martin Ferianc, Miguel Rodrigues, Hongyu Zhou, Xinyu Niu, Wayne Luk. 2021-11-30. High-Performance FPGA-based Accelerator for Bayesian Neural Networks. https://arxiv.org/abs/2105.09163

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