Intelligent Vehicle Perception: Toward the Integration on Embedded Many-core

Tiana Rakotovao 1, 2 Diego Puschini 1 Julien Mottin 1 Lukas Rummelhard 2 Amaury Negre 3 Christian Laugier 2
1 CEA-LETI - Laboratoire d'Electronique et des Technologies de l'Information
2 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
CITI - CITI Centre of Innovation in Telecommunications and Integration of services, Inria Grenoble - Rhône-Alpes
3 LIG - Laboratoire d'Informatique de Grenoble
Abstract : Intelligent vehicles (IVs) need a perception system to model the surrounding environment. The Hybrid Sampling Bayesian Occupancy Filter (HSBOF) is a perception algorithm monitoring a grid-based model of the environment called " occupancy grid ". It is a highly data-parallel algorithm and requires a high computational performance to be executed in reasonable time. It is currently implemented in CUDA on a NVIDIA GPU. However, the GPU is power consuming and its purchase cost is too high for the embedded market. In this paper, we prove that, the couple embedded many-core/OpenCL is a feasible hardware/software architecture for replacing the GPU/CUDA. Our OpenCL implementation and experimental results on a testing hardware showed that a many-core can produce an occupancy grid every 168ms while consuming 40 times less power than the GPU. The results are promising for a future integration into IVs.
Keywords : Many-core OpenCL perception system
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Submitted on : Wednesday, September 16, 2015 - 11:17:36 AM
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Tiana Rakotovao, Diego Puschini, Julien Mottin, Lukas Rummelhard, Amaury Negre, et al.. Intelligent Vehicle Perception: Toward the Integration on Embedded Many-core. 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures (PARMA), Jan 2015, Amsterdam, Netherlands. ⟨10.1145/2701310.2701313⟩. ⟨cea-01199808⟩

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