Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning on FPGAs: Past, Present, and Future   The rapid growth of data size and accessibility in recent years has
instigated a shift of philosophy in algorithm design for artificial
intelligence. Instead of engineering algorithms by hand, the ability to learn
composable systems automatically from massive amounts of data has led to
ground-breaking performance in important domains such as computer vision,
speech recognition, and natural language processing. The most popular class of
techniques used in these domains is called deep learning, and is seeing
significant attention from industry. However, these models require incredible
amounts of data and compute power to train, and are limited by the need for
better hardware acceleration to accommodate scaling beyond current data and
model sizes. While the current solution has been to use clusters of graphics
processing units (GPU) as general purpose processors (GPGPU), the use of field
programmable gate arrays (FPGA) provide an interesting alternative. Current
trends in design tools for FPGAs have made them more compatible with the
high-level software practices typically practiced in the deep learning
community, making FPGAs more accessible to those who build and deploy models.
Since FPGA architectures are flexible, this could also allow researchers the
ability to explore model-level optimizations beyond what is possible on fixed
architectures such as GPUs. As well, FPGAs tend to provide high performance per
watt of power consumption, which is of particular importance for application
scientists interested in large scale server-based deployment or
resource-limited embedded applications. This review takes a look at deep
learning and FPGAs from a hardware acceleration perspective, identifying trends
and innovations that make these technologies a natural fit, and motivates a
discussion on how FPGAs may best serve the needs of the deep learning community
moving forward.

13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat