Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Architectural Implications of Facebook's DNN-based Personalized
  Recommendation   The widespread application of deep learning has changed the landscape of
computation in the data center. In particular, personalized recommendation for
content ranking is now largely accomplished leveraging deep neural networks.
However, despite the importance of these models and the amount of compute
cycles they consume, relatively little research attention has been devoted to
systems for recommendation. To facilitate research and to advance the
understanding of these workloads, this paper presents a set of real-world,
production-scale DNNs for personalized recommendation coupled with relevant
performance metrics for evaluation. In addition to releasing a set of
open-source workloads, we conduct in-depth analysis that underpins future
system design and optimization for at-scale recommendation: Inference latency
varies by 60% across three Intel server generations, batching and co-location
of inferences can drastically improve latency-bounded throughput, and the
diverse composition of recommendation models leads to different optimization
strategies.

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The categories of this paper are: eess
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th