为了解决神经网络前向传播过程中的硬件加速问题, 设计了一套基于FPGA编程工具Vivado HLS开发的AlexNet神经网络前向传播硬件加速系统。该系统能够确保在达到相关应用要求的基础上, 有效地节省开发时间并降低开发成本。系统基于高级计算机语言C++进行FPGA电路的仿真与开发, 同时, 灵活运用具有很高便捷性及可靠性的Vivado HLS中的PIPELINE和ARRAY_PARTITION指令进行系统优化。实验结果表明, AlexNet神经网络在本文所构建的FPGA加速系统上的运行时间为21.95 ms, 比在传统GPU平台上的运行时70 ms少, 运行速度要3倍以上。此外, 每一层的网络都实现了分开封装操作, 使系统可便捷地移植到其它成熟的卷积神经网络上, 加速了深度学习在各类人工智能系统上的应用, 在智能产业具有广泛的应用价值。

To accelerate the forward-propagation process of deep-learning networks, a field-programmable gate array (FPGA) hardware-acceleration system for AlexNet was developed using Vivado High-Level Synthesis (HLS), which can greatly reduce the FPGA development cost. Using Vivado HLS, developers can design hardware architectures on an FPGA platform using C/C++ code instead of a hardware-description language. We implemented AlexNet on an FPGA platform using the HLS tool, and then used the PIPELINE and ARRAY_PARTITION directives to optimize the proposed system. An evaluation of the proposed system shows that its performance is three times better than a traditional computing-platform graphics processing unit (GPU). In the future, owing to the high-level encapsulation, the developed system can be easily transformed into other convolutional neural networks for practical operation, which shows its great portability and practical application value.