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An automotive case study on the limits of approximation for object detection

Published: 01 May 2023 Publication History

Abstract

The accuracy of camera-based object detection (CBOD) built upon deep learning is often evaluated against the real objects in frames only. However, such simplistic evaluation ignores the fact that many unimportant objects are small, distant, or background, and hence, their misdetections have less impact than those for closer, larger, and foreground objects in domains such as autonomous driving. Moreover, sporadic misdetections are irrelevant since confidence on detections is typically averaged across consecutive frames, and detection devices (e.g. cameras, LiDARs) are often redundant, thus providing fault tolerance.
This paper exploits such intrinsic fault tolerance of the CBOD process, and assesses in an automotive case study to what extent CBOD can tolerate approximation coming from multiple sources such as lower precision arithmetic, approximate arithmetic units, and even random faults due to, for instance, low voltage operation. We show that the accuracy impact of those sources of approximation is within 1% of the baseline even when considering the three approximate domains simultaneously, and hence, multiple sources of approximation can be exploited to build highly efficient accelerators for CBOD in cars.

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Published In

cover image Journal of Systems Architecture: the EUROMICRO Journal
Journal of Systems Architecture: the EUROMICRO Journal  Volume 138, Issue C
May 2023
161 pages

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Elsevier North-Holland, Inc.

United States

Publication History

Published: 01 May 2023

Author Tags

  1. Embedded systems
  2. Energy efficiency
  3. Neural network application
  4. Autonomous driving

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