Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CREW: Computation Reuse and Efficient Weight Storage for
  Hardware-accelerated MLPs and RNNs   Deep Neural Networks (DNNs) have achieved tremendous success for cognitive
applications. The core operation in a DNN is the dot product between quantized
inputs and weights. Prior works exploit the weight/input repetition that arises
due to quantization to avoid redundant computations in Convolutional Neural
Networks (CNNs). However, in this paper we show that their effectiveness is
severely limited when applied to Fully-Connected (FC) layers, which are
commonly used in state-of-the-art DNNs, as it is the case of modern Recurrent
Neural Networks (RNNs) and Transformer models.
  To improve energy-efficiency of FC computation we present CREW, a hardware
accelerator that implements Computation Reuse and an Efficient Weight Storage
mechanism to exploit the large number of repeated weights in FC layers. CREW
first performs the multiplications of the unique weights by their respective
inputs and stores the results in an on-chip buffer. The storage requirements
are modest due to the small number of unique weights and the relatively small
size of the input compared to convolutional layers. Next, CREW computes each
output by fetching and adding its required products. To this end, each weight
is replaced offline by an index in the buffer of unique products. Indices are
typically smaller than the quantized weights, since the number of unique
weights for each input tends to be much lower than the range of quantized
weights, which reduces storage and memory bandwidth requirements.
  Overall, CREW greatly reduces the number of multiplications and provides
significant savings in model memory footprint and memory bandwidth usage. We
evaluate CREW on a diverse set of modern DNNs. On average, CREW provides 2.61x
speedup and 2.42x energy savings over a TPU-like accelerator. Compared to UCNN,
a state-of-art computation reuse technique, CREW achieves 2.10x speedup and
2.08x energy savings on average.

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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: math
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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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: dg-ga