Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Communication-Optimal Convolutional Neural Nets   Efficiently executing convolutional neural nets (CNNs) is important in many
machine-learning tasks. Since the cost of moving a word of data, either between
levels of a memory hierarchy or between processors over a network, is much
higher than the cost of an arithmetic operation, minimizing data movement is
critical to performance optimization. In this paper, we present both new lower
bounds on data movement needed for CNNs, and optimal sequential algorithms that
attain these lower bounds. In most common cases, our optimal algorithms can
attain significantly more data reuse than matrix multiplication.

28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
18
The categories of this paper are: econ
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex