Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hardware-Guided Symbiotic Training for Compact, Accurate, yet
  Execution-Efficient LSTM   Many long short-term memory (LSTM) applications need fast yet compact models.
Neural network compression approaches, such as the grow-and-prune paradigm,
have proved to be promising for cutting down network complexity by skipping
insignificant weights. However, current compression strategies are mostly
hardware-agnostic and network complexity reduction does not always translate
into execution efficiency. In this work, we propose a hardware-guided symbiotic
training methodology for compact, accurate, yet execution-efficient inference
models. It is based on our observation that hardware may introduce substantial
non-monotonic behavior, which we call the latency hysteresis effect, when
evaluating network size vs. inference latency. This observation raises question
about the mainstream smaller-dimension-is-better compression strategy, which
often leads to a sub-optimal model architecture. By leveraging the
hardware-impacted hysteresis effect and sparsity, we are able to achieve the
symbiosis of model compactness and accuracy with execution efficiency, thus
reducing LSTM latency while increasing its accuracy. We have evaluated our
algorithms on language modeling and speech recognition applications. Relative
to the traditional stacked LSTM architecture obtained for the Penn Treebank
dataset, we reduce the number of parameters by 18.0x (30.5x) and measured
run-time latency by up to 2.4x (5.2x) on Nvidia GPUs (Intel Xeon CPUs) without
any accuracy degradation. For the DeepSpeech2 architecture obtained for the AN4
dataset, we reduce the number of parameters by 7.0x (19.4x), word error rate
from 12.9% to 9.9% (10.4%), and measured run-time latency by up to 1.7x (2.4x)
on Nvidia GPUs (Intel Xeon CPUs). Thus, our method yields compact, accurate,
yet execution-efficient inference models.

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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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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: hep-lat
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin