Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Bit Fully Convolutional Network for Fast Semantic Segmentation   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.

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The categories of this paper are: eess
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat