Many-core Platforms for Time-Critical Systems

信息物理系统（CPS）是下一代智能互联嵌入式系统。先进的CPS，如自动驾驶汽车，依赖于复杂的传感器处理和传感器融合能力；因此，它们需要强大的计算能力。为了满足这样的需求，越来越多的核心体系结构被认为是有吸引力的选择。提出的平台既包括传统多核架构的扩展版本，也包括复杂的片上系统，包括单指令多数据加速器，如GPU，特别适合视频处理和类似的高度并行任务。CPS是具有实时性要求的安全关键系统：未能在精确的时间窗口（截止日期）内计算所需的驱动命令可能导致灾难性后果，包括人命损失。因此，CPS中使用的计算平台必须具有时间可预测性，即能够在具有不同临界度的子系统之间提供严格的时间保证和性能隔离。不幸的是，当前的商用现货（COTS）多核架构主要针对消费者或高性能计算市场，因此在设计时没有考虑到这样的目标。

Cyber-Physical Systems (CPS) are the next generation of smart, interconnected embedded systems. Advanced CPS, such as autonomous vehicles, rely on complex sensor processing and sensor fusion capabilities; hence, they require significant computational power. To address such needs, many-core architectures are increasingly considered attractive choices. Proposed platforms include both scaled-up versions of traditional multi-core architectures, as well as complex systems-on-chip comprising single-instruction-multiple-data accelerators such as GPU, which are especially suitable for video processing and similar highly parallel tasks. CPS are safety-critical systems exhibiting real-time requirements: a failure to compute required actuation commands within a precise time window (deadline) can lead to catastrophic results, including loss of human life. For this reason, computing platforms used in CPS must be timing predictable, i.e., able to provide strict timing guarantees and performance isolation between subsystems with different criticalities. Unfortunately, current Commercial-Off-The-Shelf (COTS) many-core architectures are mainly targeted at either the consumer or high-performance computing markets, and thus are not designed with such objectives in mind.