Continental powers up supercomputer for AI training
Continental的超级计算机由50多个NVIDIA DGX系统构建，与NVIDIA Mellanox Infiniband网络相连。它根据公开可用列表进行排名Top500超级计算机作为汽车行业的顶级系统。已经选择了一种混合方法，以便能够在需要时通过云解决方案扩展容量和存储。
新超级计算机的主要任务是AI培训，深度学习，模拟和虚拟数据生成。高级驾驶员援助系统使用AI来做出决策，协助驾驶员并最终自动运行。雷达和相机等环境传感器提供原始数据。智能系统正在实时处理此原始数据，以创建车辆周围环境的全面模型，并制定了如何与环境互动的策略。最后，需要控制车辆像计划一样的行为。但是，随着系统越来越复杂，传统的软件开发方法和机器学习方法已经达到了极限。深度学习和模拟已成为基于AI解决方案的开发中的基本方法。
通过深度学习，人工神经网络使机器能够通过经验来学习，并将新信息与现有知识联系起来，从本质上模仿了人脑中的学习过程。但是，虽然一个孩子在显示几十张不同类型类型的照片后能够识别汽车，但数千个小时的培训数百万，因此需要大量的数据来训练神经网络，该神经网络以后将在辅助辅助上进行培训驾驶员甚至自动操作车辆。新的超级计算机不仅减少了此复杂过程所需的时间，还减少了新技术的营销时间。
NVIDIA SUPERCUPUTER是一台具有专门针对AI应用程序的体系结构的计算机，可帮助用户大幅度缩短开发时间。大陆谈论小时而不是几周。“The system reduces the time to train neural networks, as it allows for at least 14 times more experiments to be run at the same time,” explains Christian Schumacher, head of Program Management Systems in Continental’s Advanced Driver Assistance Systems (ADAS) business unit.
迄今为止，用于培训这些神经网络的数据主要来自大陆现实世界测试车队。目前，他们每天开车约15,000公里，收集大约100吨的数据，相当于50,000小时的电影。记录的数据已经可以通过重播并模拟物理测试驱动器来训练新系统。使用超级计算机，现在可以合成数据生成数据，这是一种高度计算的功率消耗用例，使系统可以通过模拟环境实际上从旅行中学习。
This can have several advantages for the development process: Firstly, over the long run, it might make recording, storing and mining the data generated by the physical fleet unnecessary, as necessary training scenarios can be created instantly on the system itself. Secondly, it increases speed, as virtual vehicles can travel the same number of test kilometers in a few hours that would take a real car several weeks. Thirdly, the synthetic generation of data makes it possible for systems to process and react to changing and unpredictable situations. Ultimately, this will allow vehicles to navigate safely through changing and extreme weather conditions or make reliable forecasts of pedestrian movements – thus paving the way to higher levels of automation.
扩展能力是NVIDIA DGX概念背后的主要驱动力之一。通过技术，机器可以比通过任何人类控制的方法更快，更好，更全面地学习，并且在每个进化步骤中，潜在的性能成倍增长。
The supercomputer is located in a datacenter in Frankfurt, which has been chosen for its proximity to cloud providers and, more importantly, its AI-ready environment, fulfilling specific requirements regarding cooling systems, connectivity and power supply. Certified green energy is being used to power the computer, with GPU clusters being much more energy efficient than CPU clusters by design.
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