Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SIMCNN -- Exploiting Computational Similarity to Accelerate CNN Training
  in Hardware   Convolution neural networks (CNN) are computation intensive to train. It
consists of a substantial number of multidimensional dot products between many
kernels and inputs. We observe that there are notable similarities among the
vectors extracted from inputs (i.e., input vectors). If one input vector is
similar to another one, its computations with the kernels are also similar to
those of the other and therefore, can be skipped by reusing the
already-computed results. Based on this insight, we propose a novel scheme
based on locality sensitive hashing (LSH) to exploit the similarity of
computations during CNN training in a hardware accelerator. The proposed
scheme, called SIMCNN, uses a cache (SIMCACHE) to store LSH signatures of
recent input vectors along with the computed results. If the LSH signature of a
new input vector matches with that of an already existing vector in the
SIMCACHE, the already-computed result is reused for the new vector. SIMCNN is
the first work that exploits computational similarity for accelerating CNN
training in hardware. The paper presents a detailed design, workflow, and
implementation of SIMCNN. Our experimental evaluation with four different deep
learning models shows that SIMCNN saves a significant number of computations
and therefore, improves training time up to 43%.

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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: eess