Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification   Vectored IR drop analysis is a critical step in chip signoff that checks the
power integrity of an on-chip power delivery network. Due to the prohibitive
runtimes of dynamic IR drop analysis, the large number of test patterns must be
whittled down to a small subset of worst-case IR vectors. Unlike the
traditional slow heuristic method that select a few vectors with incomplete
coverage, MAVIREC uses machine learning techniques -- 3D convolutions and
regression-like layers -- for accurately recommending a larger subset of test
patterns that exercise worst-case scenarios. In under 30 minutes, MAVIREC
profiles 100K-cycle vectors and provides better coverage than a
state-of-the-art industrial flow. Further, MAVIREC's IR drop predictor shows
10x speedup with under 4mV RMSE relative to an industrial flow.

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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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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: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom