Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GuardNN: Secure DNN Accelerator for Privacy-Preserving Deep Learning   This paper proposes GuardNN, a secure deep neural network (DNN) accelerator,
which provides strong hardware-based protection for user data and model
parameters even in an untrusted environment. GuardNN shows that the
architecture and protection can be customized for a specific application to
provide strong confidentiality and integrity protection with negligible
overhead. The design of the GuardNN instruction set reduces the TCB to just the
accelerator and enables confidentiality protection without the overhead of
integrity protection. GuardNN also introduces a new application-specific memory
protection scheme to minimize the overhead of memory encryption and integrity
verification. The scheme shows that most of the off-chip meta-data in today's
state-of-the-art memory protection can be removed by exploiting the known
memory access patterns of a DNN accelerator. GuardNN is implemented as an FPGA
prototype, which demonstrates effective protection with less than 2%
performance overhead for inference over a variety of modern DNN models.

20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ