# Training Bit Fully Convolutional Network for Fast Semantic Segmentation Source: [Training Bit Fully Convolutional Network for Fast Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:30:18.740Z Authors: He Wen, Shuchang Zhou, Zhe Liang, Yuxiang Zhang, Dieqiao Feng, Xinyu Zhou, Cong Yao Published: 2016-12-01T11:56:15.000Z ## Abstract Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability. We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations. Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation. On CPU, the dot product operation between two bit vectors can be reduced to bitwise operations and popcounts, which can offer much higher throughput than 32-bit multiplications and additions. To validate the effectiveness of BFCN, we conduct experiments on the PASCAL VOC 2012 semantic segmentation task and Cityscapes. Our BFCN with 1-bit weights and 2-bit activations, which runs 7.8x faster on CPU or requires less than 1\\% resources on FPGA, can achieve comparable performance as the 32-bit counterpart. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()