TSLA：面向自动驾驶平台语义分割的任务特定学习适配方法
TSLA: A Task-Specific Learning Adaptation for Semantic Segmentation on Autonomous Vehicles Platform
AI总结：
针对自动驾驶平台场景多样、嵌入式设备算力有限的问题，提出TSLA任务特定学习适配方法，通过三级控制机制和贝叶斯优化自动搜索参数，实现语义分割网络的定制化，兼顾算力利用与模型精度。
摘要：
自动驾驶平台会遇到多样化的驾驶场景，不同场景的硬件资源和精度要求各不相同。鉴于嵌入式设备的计算局限性，在NVIDIA® DRIVE PX 2这类目标平台上部署时，考虑计算成本至关重要。我们的目标是根据自动驾驶硬件的计算能力和特定场景，对语义分割网络进行定制化调整。我们通过三级控制机制——宽度乘数、分类器深度和分类器卷积核——实现动态适配，能够基于硬件约束和任务需求对模型组件进行细粒度控制。这种适配能力支持大范围的模型缩放、末层的针对性优化以及卷积核尺寸的场景专属优化，从而提升资源分配效率和模型性能。\n此外，我们利用带代理建模的贝叶斯优化，在计算预算紧张的情况下高效探索超参数空间。我们的方法通过自动参数搜索满足场景专属和任务特定的需求，适配自动驾驶独特的计算复杂度和精度要求。该方法为任务特定学习适配（TSLA）调整其乘加运算（MACs）规模，生成适配不同自动驾驶任务的替代配置。这些TSLA定制方案可最大化计算能力利用率和模型精度，优化硬件使用效率。
英文摘要：
Autonomous driving platforms encounter diverse driving scenarios, each with varying hardware resources and precision requirements. Given the computational limitations of embedded devices, it is crucial to consider computing costs when deploying on target platforms like the NVIDIA\textsuperscript{\textregistered} DRIVE PX 2. Our objective is to customize the semantic segmentation network according to the computing power and specific scenarios of autonomous driving hardware. We implement dynamic adaptability through a three-tier control mechanism -- width multiplier, classifier depth, and classifier kernel -- allowing fine-grained control over model components based on hardware constraints and task requirements. This adaptability facilitates broad model scaling, targeted refinement of the final layers, and scenario-specific optimization of kernel sizes, leading to improved resource allocation and performance. Additionally, we leverage Bayesian Optimization with surrogate modeling to efficiently explore hyperparameter spaces under tight computational budgets. Our approach addresses scenario-specific and task-specific requirements through automatic parameter search, accommodating the unique computational complexity and accuracy needs of autonomous driving. It scales its Multiply-Accumulate Operations (MACs) for Task-Specific Learning Adaptation (TSLA), resulting in alternative configurations tailored to diverse self-driving tasks. These TSLA customizations maximize computational capacity and model accuracy, optimizing hardware utilization.
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