It is always well believed that Binary Neural Networks (BNNs) could
drastically accelerate the inference efficiency by replacing the arithmetic
operations in float-valued Deep Neural Networks (DNNs) with bit-wise
operations. Nevertheless, there has not been open-source implementation in
support of this idea on low-end ARM devices (e.g., mobile phones and embedded
devices). In this work, we propose daBNN --- a super fast inference framework
that implements BNNs on ARM devices. Several speed-up and memory refinement
strategies for bit-packing, binarized convolution, and memory layout are
uniquely devised to enhance inference efficiency. Compared to the recent
open-source BNN inference framework, BMXNet, our daBNN is
$7\times$$\sim$$23\times$ faster on a single binary convolution, and about
$6\times$ faster on Bi-Real Net 18 (a BNN variant of ResNet-18). The daBNN is a
BSD-licensed inference framework, and its source code, sample projects and
pre-trained models are available on-line: this https URL

本文提出了 daBNN 这一快速推理框架，基于二进制神经网络，通过特定的速度优化和内存优化策略，可以有效地在 ARM 设备上运行，并在单个二值卷积上比最近的 BMXNet 布尔神经网络推理框架快 7 倍到 23 倍，并在 Bi-Real Net 18 上比 BMXNet 快 6 倍。