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%.

Robustifying the deployment of tinyML models for autonomous mini-vehicles / de Prado, M.; Rusci, M.; Donze, R.; Capotondi, A.; Monnerat, S.; Benini, L.; Pazos, N.. - 2021-:(2021), pp. 1-5. ((Intervento presentato al convegno 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 tenutosi a Daegu, kor nel 22 - 28 May 2021 [10.1109/ISCAS51556.2021.9401154].

Robustifying the deployment of tinyML models for autonomous mini-vehicles

Capotondi A.;
2021

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%.
53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Daegu, kor
22 - 28 May 2021
2021-
1
5
de Prado, M.; Rusci, M.; Donze, R.; Capotondi, A.; Monnerat, S.; Benini, L.; Pazos, N.
Robustifying the deployment of tinyML models for autonomous mini-vehicles / de Prado, M.; Rusci, M.; Donze, R.; Capotondi, A.; Monnerat, S.; Benini, L.; Pazos, N.. - 2021-:(2021), pp. 1-5. ((Intervento presentato al convegno 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 tenutosi a Daegu, kor nel 22 - 28 May 2021 [10.1109/ISCAS51556.2021.9401154].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1280828
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