Bayesian Neural Networks (BayesNNs) have demonstrated their capability of providing calibrated prediction for safety-critical applications such as medical imaging and autonomous driving. However, the high algorithmic complexity and the poor hardware performance of BayesNNs hinder their deployment in real-life applications. To bridge this gap, this paper proposes a novel multi-exit Monte-Carlo Dropout (MCD)-based BayesNN that achieves well-calibrated predictions with low algorithmic complexity. To further reduce the barrier to adopting BayesNNs, we propose a transformation framework that can generate FPGA-based accelerators for multi-exit MCD-based BayesNNs. Several novel optimization techniques are introduced to improve hardware performance. Our experiments demonstrate that our auto-generated accelerator achieves higher energy efficiency than CPU, GPU, and other state-of-the-art hardware implementations.

本研究提出了一种新颖的多出口Monte-Carlo Dropout（MCD）Bayesian神经网络，实现了具有低算法复杂度的良好校准预测。此外，我们还提出了一个转换框架，可为多出口MCD BayesNN生成基于FPGA的加速器，以进一步降低采用BayesNNs的障碍。通过引入几种新颖的优化技术，我们的实验表明，我们的自动生成的加速器比CPU、GPU和其他最先进的硬件实现具有更高的能量效率。

当蒙特卡罗 Dropout 遇到多出口: 在 FPGA 上优化贝叶斯神经网络