Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Design Automation for Efficient Deep Learning Computing   Efficient deep learning computing requires algorithm and hardware co-design
to enable specialization: we usually need to change the algorithm to reduce
memory footprint and improve energy efficiency. However, the extra degree of
freedom from the algorithm makes the design space much larger: it's not only
about designing the hardware but also about how to tweak the algorithm to best
fit the hardware. Human engineers can hardly exhaust the design space by
heuristics. It's labor consuming and sub-optimal. We propose design automation
techniques for efficient neural networks. We investigate automatically
designing specialized fast models, auto channel pruning, and auto
mixed-precision quantization. We demonstrate such learning-based, automated
design achieves superior performance and efficiency than rule-based human
design. Moreover, we shorten the design cycle by 200x than previous work, so
that we can afford to design specialized neural network models for different
hardware platforms.

24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph