Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BED: A Real-Time Object Detection System for Edge Devices   Deploying machine learning models to edge devices has many real-world
applications, especially for the scenarios that demand low latency, low power,
or data privacy. However, it requires substantial research and engineering
efforts due to the limited computational resources and memory of edge devices.
In this demo, we present BED, an object detection system for edge devices
practiced on the MAX78000 DNN accelerator. BED integrates on-device DNN
inference with a camera and a screen for image acquisition and output
exhibition, respectively. Experiment results indicate BED can provide accurate
detection with an only 300KB tiny DNN model.

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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio