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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
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Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles

机译：强调自动迷你车辆的Tinyml模型的部署

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摘要

Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training. To address these challenges, we propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target deployment environment in-the-loop. We leverage a family of compact and high-throughput tinyCNNs to control the mini-vehicle that learn by imitating a computer vision algorithm, i.e., the expert, in the target environment. Thus, the tinyCNNs, having only access to an on-board fast-rate linear camera, gain robustness to lighting conditions and improve over time. Moreover, we introduce an online predictor that can choose between different tinyCNN models at runtime—trading accuracy and latency—which minimises the inference’s energy consumption by up to 3.2×. Finally, we leverage GAP8, a parallel ultra-low-power RISC-V-based micro-controller unit (MCU), to meet the real-time inference requirements. When running the family of tinyCNNs, our solution running on GAP8 outperforms any other implementation on the STM32L4 and NXP k64f (traditional single-core MCUs), reducing the latency by over 13× and the energy consumption by 92%.
机译：由于深度学习的突破，标准大小的自治车辆迅速提高。然而，由于它们的板载存储和计算能力有限，缩放到迷你车辆的自动驾驶造成了几个挑战。此外，当部署在动态环境中，自主系统缺乏鲁棒性，其中基本的分布与培训期间的分布不同。为了解决这些挑战，我们提出了一个闭环学习流程，用于自动驾驶迷你车辆，包括循环中的目标部署环境。我们利用一系列紧凑型和高吞吐量的Tinycnns来控制通过模仿计算机视觉算法，即专家在目标环境中学习的迷你车辆。因此，只有访问车载快速线性相机的Tinycnns，会对照明条件增强鲁棒性并随时间改善。此外，我们介绍了一个在线预测指标，可以在运行时交易准确度和延迟的不同TINYCNN模型中选择 - 这最小化了推理的能量消耗高达3.2倍。最后，我们利用GAP8，一个平行的超低功耗RISC-V基微控制器单元（MCU），以满足实时推理要求。运行TinyCnns系列时，我们在GAP8上运行的解决方案优于STM32L4和NXP K64F（传统单核MCU）上的任何其他实现，从而将延迟减少超过13倍，并且能量消耗量为92％。

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