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Title: Re-Thinking CNN Frameworks for Time-Sensitive Autonomous-Driving Applications: Addressing an Industrial Challenge
Vision-based perception systems are crucial for profitable autonomous-driving vehicle products. High accuracy in such perception systems is being enabled by rapidly evolving convolution neural networks (CNNs). To achieve a better understanding of its surrounding environment, a vehicle must be provided with full coverage via multiple cameras. However, when processing multiple video streams, existing CNN frameworks often fail to provide enough inference performance, particularly on embedded hardware constrained by size, weight, and power limits. This paper presents the results of an industrial case study that was conducted to re-think the design of CNN software to better utilize available hardware resources. In this study, techniques such as parallelism, pipelining, and the merging of per-camera images into a single composite image were considered in the context of a Drive PX2 embedded hardware platform. The study identifies a combination of techniques that can be applied to increase throughput (number of simultaneous camera streams) without significantly increasing per-frame latency (camera to CNN output) or reducing per-stream accuracy.  more » « less
Award ID(s):
1717589 1837337
NSF-PAR ID:
10108220
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)
Page Range / eLocation ID:
305 to 317
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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