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.

翻译：贝叶斯神经网络在医学影像和自动驾驶等安全关键应用中展现了提供校准预测的能力。然而，贝叶斯神经网络的高算法复杂度与较差的硬件性能阻碍了其在实际场景中的部署。为弥合这一差距，本文提出一种新型多出口蒙特卡洛丢弃法贝叶斯神经网络，该网络以低算法复杂度实现了良好校准的预测。为进一步降低贝叶斯神经网络的采用门槛，我们提出一个转换框架，可为基于多出口MCD的贝叶斯神经网络生成FPGA加速器。文中引入了多项创新优化技术以提升硬件性能。实验表明，与CPU、GPU及其他先进硬件实现相比，我们自动生成的加速器实现了更高的能效。