Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hardware-oriented Approximation of Convolutional Neural Networks   High computational complexity hinders the widespread usage of Convolutional
Neural Networks (CNNs), especially in mobile devices. Hardware accelerators are
arguably the most promising approach for reducing both execution time and power
consumption. One of the most important steps in accelerator development is
hardware-oriented model approximation. In this paper we present Ristretto, a
model approximation framework that analyzes a given CNN with respect to
numerical resolution used in representing weights and outputs of convolutional
and fully connected layers. Ristretto can condense models by using fixed point
arithmetic and representation instead of floating point. Moreover, Ristretto
fine-tunes the resulting fixed point network. Given a maximum error tolerance
of 1%, Ristretto can successfully condense CaffeNet and SqueezeNet to 8-bit.
The code for Ristretto is available.

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The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn