Optimizing Memory for Advanced Driver Assistance Systems and Autonomous Driving

优化高级驾驶辅助系统和自动驾驶的内存

• 批准号: 426328834
• 负责人: Professor Dr.-Ing. Norbert Wehn
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Research Grants (Transfer Project)
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/426328834?language=en
• 关键词: Optimizing; Memory; Advanced; Driver; Assistance

Heterogeneous multi-core architectures enhanced by custom accelerator cores are today widely used in many embedded applications. These types of compute platforms that were originally developed for consumer applications are now entering safety critical applications, especially in the automotive domain where autonomous driving is currently disrupting the conventional automotive electronics development. The immense computing power of such architectures brings further large challenges. The increasing gap between speed of the heterogeneous multi-core architectures and accesses to the main memories poses a severe limit. The dominant type of main memories are Dynamic Random Access Memories (DRAMs) that offer the best trade-off between storage density and access times. Algorithms for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) in automotive require low latency and huge external memory bandwidth. Thus, the memory bandwidth becomes one of the big bottlenecks. DRAMs are commodity devices optimized for minimum cost per storage bit. Hence, the DRAM package has to be cheap that limits the available package pins. Furthermore, DRAMs have a complex internal architecture with advanced internal prefetching to bridge the gap between external available memory bandwidth and internal latency. DRAM technologies exhibit a large parameter variation (speed sorting) and the storage cells have to be refreshed, which are very sensitive to temperature. These features make it very challenging to use DRAMs in safety critical applications. In recent years many new DRAM memory devices have been presented (e.g. DDR4, LPDDR4, GDDR6, Wide I/O, HMB2). It is not yet clear how these memory modules have to be used and how they will perform in the automotive context with respect to bandwidth, latency, power, temperature, reliability, safety and security. The scientific DRAM research so far mainly focused on mobile devices and data centers. These applications have totally different profiles compared to the safety critical automotive domain. Thus, there is a high need to close this research gap by transferring basic research to industry, taking the automotive application requirements into account. To the best of our knowledge, there are no investigations or publications that optimize the DRAM memory subsystem with respect to future automotive applications. Therefore, in this trilateral transfer project, the results gained from the basic research at TU Kaiserslautern about DRAM modelling, optimization, controllers etc. will be further developed towards applicability for automotive industry. The Fraunhofer IESE will support and coordinate this transfer with their strong background in safety for automotive and embedded systems. The application partner Bosch, one of the major automotive suppliers, will provide detailed application know-how, requirements and concrete research challenges from an industry perspective.

由定制加速器内核增强的异构多核架构如今广泛用于许多嵌入式应用中。这些类型的计算平台最初是为消费者应用开发的，现在正在进入安全关键应用，特别是在汽车领域，自动驾驶目前正在破坏传统的汽车电子开发。这种架构的巨大计算能力带来了更大的挑战。异构多核架构的速度和对主存的访问之间的差距越来越大，这造成了严重的限制。主存储器的主要类型是动态随机存取存储器（DRAM），它在存储密度和存取时间之间提供了最佳的平衡。用于汽车中的高级驾驶辅助系统（ADAS）和自动驾驶（AD）的算法需要低延迟和巨大的外部存储器带宽。因此，内存带宽成为最大的瓶颈之一。DRAM是针对每个存储位的最小成本而优化的商品设备。因此，DRAM封装必须便宜，这限制了可用的封装引脚。此外，DRAM具有复杂的内部架构，其具有高级内部预取以桥接外部可用存储器带宽与内部延迟之间的差距。DRAM技术表现出大的参数变化（速度排序），并且存储单元必须被刷新，这对温度非常敏感。这些特征使得在安全关键应用中使用DRAM非常具有挑战性。近年来，已经提出了许多新的DRAM存储器设备（例如，DDR4、LPDDR4、GDDR 6、宽I/O、HMB 2）。目前尚不清楚这些内存模块必须如何使用，以及它们在带宽、延迟、功率、温度、可靠性、安全性和安全性方面在汽车环境中的表现如何。到目前为止，DRAM的科学研究主要集中在移动的设备和数据中心。与安全关键的汽车领域相比，这些应用具有完全不同的特征。因此，迫切需要通过将基础研究转移到工业中来缩小这一研究差距，同时考虑到汽车应用的要求。据我们所知，目前还没有针对未来汽车应用优化DRAM存储器子系统的研究或出版物。因此，在这个三边转移项目中，从TU Kaiserlestern关于DRAM建模，优化，控制器等基础研究中获得的结果将进一步发展到汽车行业的适用性。Fraunhofer IESE将以其在汽车和嵌入式系统安全方面的强大背景支持和协调这一转移。应用合作伙伴博世是主要的汽车供应商之一，将从行业角度提供详细的应用知识、要求和具体的研究挑战。