FPGA-based accelerator for losslessly quantized convolutional neural networks
FPGA-based accelerator for losslessly quantized convolutional neural networks
复制标题
基于 FPGA 的无损量化卷积神经网络加速器
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
H. Amano
中科院分区：
文献类型：
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作者：
Man;Ryosuke Kazami;H. Amano
卷积神经网络（CNN）已被广泛用于各种计算机视觉任务。尽管GPU是CNN实施的最常见平台，但FPGA是提供更好的能源效率的有希望的替代方法。最近的工作表明，网络量化的潜力减小模型大小并提高计算效率，同时保持与完整精度对应物的可比精度。量化的CNN特别适合于FPGA实施，因为存在具有非平凡位宽的值。在本文中，我们介绍了使用高级合成工具的基于FPGA基于FPGA的加速器的设计。实验结果表明，对于Imagnet数据集，我们的设计实现了12.9 GOPS/WATT的ALEXNET。
Convolutional Neural Networks (CNN) have been widely used for various computer vision tasks. While GPUs are the most common platform for CNN implementation, FPGAs are promising alternatives to provide better energy efficiency. Recent work demonstrates the potential of network quantization to reduce the model size and enhance computation efficiency while maintaining comparable accuracy to the full precision counterparts. Quantized CNN is especially suitable for FPGA implementation due to the presence of values with non-trivial bitwidth. In this paper, we present the design of an FPGA-based accelerator for losslessly quantized CNNs using High Level Synthesis tool. The experiment result shows that our design achieves 12.9 GOPS/Watt for quantized Alexnet on Imagnet Dataset.