Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parallel Multi Channel Convolution using General Matrix Multiplication   Convolutional neural networks (CNNs) have emerged as one of the most
successful machine learning technologies for image and video processing. The
most computationally intensive parts of CNNs are the convolutional layers,
which convolve multi-channel images with multiple kernels. A common approach to
implementing convolutional layers is to expand the image into a column matrix
(im2col) and perform Multiple Channel Multiple Kernel (MCMK) convolution using
an existing parallel General Matrix Multiplication (GEMM) library. This im2col
conversion greatly increases the memory footprint of the input matrix and
reduces data locality.
  In this paper we propose a new approach to MCMK convolution that is based on
General Matrix Multiplication (GEMM), but not on im2col. Our algorithm
eliminates the need for data replication on the input thereby enabling us to
apply the convolution kernels on the input images directly. We have implemented
several variants of our algorithm on a CPU processor and an embedded ARM
processor. On the CPU, our algorithm is faster than im2col in most cases.

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The categories of this paper are: patt-sol
18
The categories of this paper are: econ
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat