FPGA becomes a popular technology for implementing Convolutional Neural
Network (CNN) in recent years. Most CNN applications on FPGA are
domain-specific, e.g., detecting objects from specific categories, in which
commonly-used CNN models pre-trained on general datasets may not be efficient
enough. This paper presents TuRF, an end-to-end CNN acceleration framework to
efficiently deploy domain-specific applications on FPGA by transfer learning
that adapts pre-trained models to specific domains, replacing standard
convolution layers with efficient convolution blocks, and applying layer fusion
to enhance hardware design performance. We evaluate TuRF by deploying a
pre-trained VGG-16 model for a domain-specific image recognition task onto a
Stratix V FPGA. Results show that designs generated by TuRF achieve better
performance than prior methods for the original VGG-16 and ResNet-50 models,
while for the optimised VGG-16 model TuRF designs are more accurate and easier
to process.

本文提出了 TuRF 框架，通过迁移学习将预训练模型适应于特定领域，替换普通卷积层并应用层融合来提高硬件设计性能，从而在 FPGA 上有效部署特定领域的应用，评估结果表明，与原始模型以及其他先前方法相比，TuRF 可以更好地实现 VGG-16 模型的性能，同时更准确和易于处理。

面向 FPGA 的领域特定应用的高效卷积神经网络

Towards Efficient Convolutional Neural Network for Domain-Specific  Applications on FPGA

Many infrastructure-free indoor positioning systems rely on fine-grained
location-dependent fingerprints to train models for localization. The site
survey process to collect fingerprints is laborious and is considered one of
the major obstacles to deploying such systems. In this paper, we propose TuRF,
a fast path-based fingerprint collection mechanism for site survey. We
demonstrate the feasibility to collect fingerprints for indoor localization
during walking along predefined paths. A step counter is utilized to
accommodate the variations in walking speed. Approximate location labels
inferred from the steps are then used to train a Gaussian Process regression
model. Extensive experiments show that TuRF can significantly reduce the
required time for site survey, without compromising the localization
performance.

本研究提出了基于路径的指纹采集机制 TuRF，可通过步数计算近似位置标签用于高斯过程回归模型的训练，有效提高了室内定位的指纹采集效率和定位性能。