Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deployment of Deep Neural Networks for Object Detection on Edge AI
  Devices with Runtime Optimization   Deep neural networks have proven increasingly important for automotive scene
understanding with new algorithms offering constant improvements of the
detection performance. However, there is little emphasis on experiences and
needs for deployment in embedded environments. We therefore perform a case
study of the deployment of two representative object detection networks on an
edge AI platform. In particular, we consider RetinaNet for image-based 2D
object detection and PointPillars for LiDAR-based 3D object detection. We
describe the modifications necessary to convert the algorithms from a PyTorch
training environment to the deployment environment taking into account the
available tools. We evaluate the runtime of the deployed DNN using two
different libraries, TensorRT and TorchScript. In our experiments, we observe
slight advantages of TensorRT for convolutional layers and TorchScript for
fully connected layers. We also study the trade-off between runtime and
performance, when selecting an optimized setup for deployment, and observe that
quantization significantly reduces the runtime while having only little impact
on the detection performance.

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The categories of this paper are: atom-ph
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The categories of this paper are: cmp-lg
2
The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas