Over the last years, a great success of deep neural networks (DNNs) has been
witnessed in computer vision and other fields. However, performance and power
constraints make it still challenging to deploy DNNs on mobile devices due to
their high computational complexity. Binary neural networks (BNNs) have been
demonstrated as a promising solution to achieve this goal by using bit-wise
operations to replace most arithmetic operations. Currently, existing
GPU-accelerated implementations of BNNs are only tailored for desktop
platforms. Due to architecture differences, mere porting of such
implementations to mobile devices yields suboptimal performance or is
impossible in some cases. In this paper, we propose PhoneBit, a GPU-accelerated
BNN inference engine for Android-based mobile devices that fully exploits the
computing power of BNNs on mobile GPUs. PhoneBit provides a set of
operator-level optimizations including locality-friendly data layout, bit
packing with vectorization and layers integration for efficient binary
convolution. We also provide a detailed implementation and parallelization
optimization for PhoneBit to optimally utilize the memory bandwidth and
computing power of mobile GPUs. We evaluate PhoneBit with AlexNet, YOLOv2 Tiny
and VGG16 with their binary version. Our experiment results show that PhoneBit
can achieve significant speedup and energy efficiency compared with
state-of-the-art frameworks for mobile devices.

本文提出了 PhoneBit，一种针对基于 Android 的移动设备的 GPU 加速二值神经网络推断引擎，旨在通过运用区别于桌面平台的架构，提出操作级优化方案，以挖掘移动 GPU 的计算能力，实现二值卷积的高效率，从而取得了相比移动设备上其它顶尖框架更为显著的速度加速和能量效率改进。