Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Gradient-Interleaved Scheduler for Energy-Efficient Backpropagation
  for Training Neural Networks   This paper addresses design of accelerators using systolic architectures for
training of neural networks using a novel gradient interleaving approach.
Training the neural network involves backpropagation of error and computation
of gradients with respect to the activation functions and weights. It is shown
that the gradient with respect to the activation function can be computed using
a weight-stationary systolic array while the gradient with respect to the
weights can be computed using an output-stationary systolic array. The novelty
of the proposed approach lies in interleaving the computations of these two
gradients to the same configurable systolic array. This results in reuse of the
variables from one computation to the other and eliminates unnecessary memory
accesses. The proposed approach leads to 1.4 - 2.2 times savings in terms of
number of cycles and $1.9 \times$ savings in terms of memory accesses. Thus,
the proposed accelerator reduces latency and energy consumption.

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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
2
The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio