Design Space Exploration of Semantic Segmentation CNN SalsaNext for Constrained Architectures

verfasst von

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.

Organisationseinheit(en)

Fachgebiet Architekturen und Systeme

Typ

Aufsatz in Konferenzband

Seiten

28-29

Anzahl der Seiten

2

Publikationsdatum

2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Hardware und Architektur, Computernetzwerke und -kommunikation

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.1109/asap61560.2024.00016 (Zugang: Geschlossen)