Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cortex: A Compiler for Recursive Deep Learning Models   Optimizing deep learning models is generally performed in two steps: (i)
high-level graph optimizations such as kernel fusion and (ii) low level kernel
optimizations such as those found in vendor libraries. This approach often
leaves significant performance on the table, especially for the case of
recursive deep learning models. In this paper, we present Cortex, a
compiler-based approach to generate highly-efficient code for recursive models
for low latency inference. Our compiler approach and low reliance on vendor
libraries enables us to perform end-to-end optimizations, leading to up to 14X
lower inference latencies over past work, across different backends.

14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol