Low-precision techniques can effectively reduce the computational complexity and bandwidth requirements of a convolutional neural network (CNN) inference, but may lead to significant accuracy degradation. Mixed-low-precision techniques provide a superior approach for CNN inference since it can take the advantages of low precision while maintaining accuracy. In this article, we propose a high-performance, highly flexible ${W^8A^8}$W8A8 (INT8 weight and INT8 activation) and ${W^T A^2}$WTA2 (TERNARY weight and INT2 activation) mixed-precision CNN inference hardware architecture, DPUmxp, designed and implemented on Xilinx Virtex UltraScale+13P FPGA with peak performance up to 58.9 TOPS.

中文翻译：

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FPGA 上的高性能混合低精度 CNN 推理加速器


低精度技术可以有效降低卷积神经网络（CNN）推理的计算复杂性和带宽要求，但可能会导致精度显着下降。混合低精度技术为 CNN 推理提供了一种优越的方法，因为它可以在保持准确性的同时利用低精度的优势。在本文中，我们提出了一种高性能、高度灵活的${W^8A^8}$W8A8（INT8权重和INT8激活）和${W^TA^2}$WTA2（TERNARY权重和INT2激活）混合-精密CNN推理硬件架构DPUmxp，在Xilinx Virtex UltraScale+13P FPGA上设计和实现，峰值性能高达58.9 TOPS。