Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Precise neural network computation with imprecise analog devices   The operations used for neural network computation map favorably onto simple
analog circuits, which outshine their digital counterparts in terms of
compactness and efficiency. Nevertheless, such implementations have been
largely supplanted by digital designs, partly because of device mismatch
effects due to material and fabrication imperfections. We propose a framework
that exploits the power of deep learning to compensate for this mismatch by
incorporating the measured device variations as constraints in the neural
network training process. This eliminates the need for mismatch minimization
strategies and allows circuit complexity and power-consumption to be reduced to
a minimum. Our results, based on large-scale simulations as well as a prototype
VLSI chip implementation indicate a processing efficiency comparable to current
state-of-art digital implementations. This method is suitable for future
technology based on nanodevices with large variability, such as memristive
arrays.

3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph