论文标题
鲁棒化的自动迷你车辆的Tinyml模型的部署
Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles
论文作者
论文摘要
由于深度学习的突破，标准大小的自动驾驶汽车迅速改善。但是，将自动驾驶到部署在动态环境上的低功率系统的规模构成挑战，以阻止其采用。为了解决这些问题，我们提出了一个闭环学习流，用于自动驾驶迷你车，其中包括目标环境。我们利用一个紧凑型和高通量的小型tinycnns来控制迷你车辆，该车辆通过模仿计算机视觉算法，即专家，在目标环境中学习。因此，仅使用船上快速线性摄像头，可以使照明条件获得稳健性并随着时间的推移而改善。此外，我们利用平行的超低功率RISC-V SOC GAP8来满足推理要求。在运行CNN家族时，我们的GAP8解决方案的表现优于STM32L4和NXP K64F（Cortex-M4）上的任何其他实现，将延迟降低了13倍，能量完善降低了92％。
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%.
