Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Digital Electronics and Analog Photonics for Convolutional Neural
  Networks (DEAP-CNNs)   Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools
for extracting features from large datasets for applications such as computer
vision and natural language processing. However, a convolution is a
computationally expensive operation in digital electronics. In contrast,
neuromorphic photonic systems, which have experienced a recent surge of
interest over the last few years, propose higher bandwidth and energy
efficiencies for neural network training and inference. Neuromorphic photonics
exploits the advantages of optical electronics, including the ease of analog
processing, and busing multiple signals on a single waveguide at the speed of
light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN
hardware architecture that has potential to be 2.8 to 14 times faster while
maintaining the same power usage of current state-of-the-art GPUs.

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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex