Design Space Exploration of Semantic Segmentation CNN SalsaNext for Constrained Architectures

authored by

Oliver Renke, Christoph Riggers, Jens Karrenbauer, Holger Blume

Abstract

The growing use of LiDAR systems and constrained computing resources in the automotive sector require efficient LiDAR processing. SalsaNext, a convolutional neural network for semantic segmentation, is a promising candidate for deployment in that area. To extend the research regarding its quantization and investigate its adaptability to constrained resources, a design space exploration is performed. The design space, defined by model size, topology, and compute precision, is evaluated on a Jetson AGX Orin regarding classification accuracy, latency, and energy efficiency. The results display a trade-off between classification accuracy and runtime. The smallest model evaluated in INT8 on the GPU provides the smallest latency of 14.48 ms with a mloU score of 43.2%. A mloU score of 47.7% at a latency of 26.92 ms can be achieved with the medium-sized model and modified topology evaluated in INT8 on the DLA. The medium-sized model with modified topology provides good classification accuracy evaluated in FP32 on the GPU with a mloU score of 55.2% in 67.85 ms.

Organisation(s)

Architectures and Systems Section

Type

Conference contribution

Pages

28-29

No. of pages

2

Publication date

2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Hardware and Architecture, Computer Networks and Communications

Sustainable Development Goals

SDG 7 - Affordable and Clean Energy

Electronic version(s)

https://doi.org/10.1109/asap61560.2024.00016 (Access: Closed)