Abstract
Neural architecture search (NAS) has shown great promise in automatically designing lightweight models. However, conventional approaches are insufficient in training the supernet and pay little attention to actual robot hardware resources. To meet such challenges, we propose RAM-NAS, a resource-aware multi-objective NAS method that focuses on improving the supernet pretrain and resource-awareness on robot hardware devices. We introduce the concept of subnets mutual distillation, which refers to mutually distilling all subnets sampled by the sandwich rule. Additionally, we utilize the Decoupled Knowledge Distillation (DKD) loss to enhance logits distillation performance. To expedite the search process with consideration for hardware resources, we used data from three types of robotic edge hardware to train Latency Surrogate predictors. These predictors facilitated the estimation of hardware inference latency during the search phase, enabling a unified multi-objective evolutionary search to balance model accuracy and latency trade-offs. Our discovered model family, RAM-NAS models, can achieve top-1 accuracy ranging from 76.7% to 81.4% on ImageNet. In addition, the resource-aware multi-objective NAS we employ significantly reduces the model's inference latency on edge hardware for robots. We conducted experiments on downstream tasks to verify the scalability of our methods. The inference time for detection and segmentation is reduced on all three hardware types compared to MobileNetv3-based methods. Our work fills the gap in NAS for robot hardware resource-aware.
Abstract (translated)
神经架构搜索（NAS）在自动设计轻量级模型方面展现了巨大的潜力。然而，传统方法在训练超网和关注实际机器人硬件资源方面存在不足。为应对这些挑战，我们提出了一种新的资源感知多目标NAS方法——RAM-NAS，旨在改进超网的预训练并提高对机器人硬件设备的认识度。 该方法引入了子网络互蒸馏的概念，即根据三明治规则抽样的所有子网络之间的相互蒸馏过程。此外，我们采用解耦知识蒸馏（DKD）损失来增强日志概率蒸馏的效果。为了加速考虑硬件资源的搜索进程，我们利用来自三种不同机器人边缘硬件的数据训练了时延替代预测器。这些预测器在搜索阶段能够估算硬件推理延迟，从而实现统一多目标进化搜索以平衡模型精度与时延之间的权衡。 通过RAM-NAS方法发现的模型系列，在ImageNet数据集上的Top-1准确率范围为76.7%到81.4%之间。此外，我们所采用的资源感知多目标NAS显著降低了机器人边缘硬件上模型的推理延迟时间。我们在下游任务中进行了实验以验证该方法的可扩展性，结果显示相较于基于MobileNetv3的方法，在三种不同的硬件类型上检测和分割任务的推理时间均有所减少。 我们的工作填补了NAS在机器人硬件资源感知方面的空白，为今后的研究提供了宝贵的参考与借鉴。
URL
https://arxiv.org/abs/2509.20688
