Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Run-Time Efficient RNN Compression for Inference on Edge Devices   Recurrent neural networks can be large and compute-intensive, yet many
applications that benefit from RNNs run on small devices with very limited
compute and storage capabilities while still having run-time constraints. As a
result, there is a need for compression techniques that can achieve significant
compression without negatively impacting inference run-time and task accuracy.
This paper explores a new compressed RNN cell implementation called Hybrid
Matrix Decomposition (HMD) that achieves this dual objective. This scheme
divides the weight matrix into two parts - an unconstrained upper half and a
lower half composed of rank-1 blocks. This results in output features where the
upper sub-vector has "richer" features while the lower-sub vector has
"constrained features". HMD can compress RNNs by a factor of 2-4x while having
a faster run-time than pruning (Zhu &Gupta, 2017) and retaining more model
accuracy than matrix factorization (Grachev et al., 2017). We evaluate this
technique on 5 benchmarks spanning 3 different applications, illustrating its
generality in the domain of edge computing.

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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom