Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles

• URL: http://arxiv.org/abs/2007.00302v2
• Date: Sat, 13 Feb 2021 20:38:02 GMT
• Title: Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
• Authors: Miguel de Prado, Manuele Rusci, Romain Donze, Alessandro Capotondi, Serge Monnerat, Luca Benini and, Nuria Pazos
• Abstract summary: We propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target environment in-the-loop. We leverage a family of tinyCNNs to control the mini-vehicle, which learn in the target environment by imitating a computer vision algorithm, i.e., the expert. When running the family of CNNs, our solution outperforms any other implementation on the STM32L4 and k64f (Cortex-M4), reducing the latency by over 13x and the energy consummation by 92%.
• Score: 61.27933385742613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Standard-size autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to low-power systems deployed on dynamic environments poses several challenges that prevent their adoption. To address them, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle, which learn in the target environment by imitating a computer vision algorithm, i.e., the expert. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Further, we leverage GAP8, a parallel ultra-low-power RISC-V SoC, to meet the inference requirements. When running the family of CNNs, our GAP8's solution outperforms any other implementation on the STM32L4 and NXP k64f (Cortex-M4), reducing the latency by over 13x and the energy consummation by 92%.

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