Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Binary Neural Networks as a general-propose compute paradigm for
  on-device computer vision   For binary neural networks (BNNs) to become the mainstream on-device computer
vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than
8-bit quantization and establish a similar degree of general applicability in
vision tasks. To this end, we propose a BNN framework comprising 1) a
minimalistic inference scheme for hardware-friendliness, 2) an
over-parameterized training scheme for high accuracy, and 3) a simple procedure
to adapt to different vision tasks. The resultant framework overtakes 8-bit
quantization in the speed-vs-accuracy tradeoff for classification, detection,
segmentation, super-resolution and matching: our BNNs not only retain the
accuracy levels of their 8-bit baselines but also showcase 1.3-2.4$\times$
faster FPS on mobile CPUs. Similar conclusions can be drawn for prototypical
systolic-array-based AI accelerators, where our BNNs promise 2.8-7$\times$
fewer execution cycles than 8-bit and 2.1-2.7$\times$ fewer cycles than
alternative BNN designs. These results suggest that the time for large-scale
BNN adoption could be upon us.

26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom