Industrial domains such as automotive, robotics, and aerospace are rapidly evolving to satisfy the increasing demand for machine-learning-driven Autonomy, Connectivity, Electrification, and Shared mobility (ACES). This paradigm shift inherently and significantly increases the requirement for onboard computing performance and high-performance communication infrastructure. At the same time, Moore's Law and Dennard Scaling are grinding to a halt, in turn, driving computing systems to larger scales and higher levels of heterogeneity and specialization, through application-specific hardware accelerators, instead of relying on technological scaling only. Approaching ACES requires this substantial amount of compute at an increasingly high energy-efficiency, since most use cases are fundamentally resource-bound. This increase in compute performance and heterogeneity goes hand in hand with a growing demand for high memory bandwidth and capacity as the driving applications grow in complexity, operating on huge and progressively irregular data sets and further requiring a steady influx of sensor data, increasing pressure both on on-chip and off-chip interconnect systems. Further, ACES combines real-time time-critical with general compute tasks on the same physical platform, sharing communication, storage, and micro-architectural resources. These heterogeneous mixed-criticality systems (MCSs) place additional pressure on the interconnect, demanding minimal contention between the different criticality levels to sustain a high degree of predictability. Fulfilling the performance and energy-efficiency requirements across a wide range of industrial applications requires a carefully co-designed process of the memory system with the use cases as well as the compute units and accelerators.

翻译：汽车、机器人与航空航天等工业领域正快速发展，以满足日益增长的由机器学习驱动的自主性、互联性、电气化与共享出行（ACES）需求。这一范式转变从本质上显著提升了车载计算性能与高性能通信基础设施的要求。与此同时，摩尔定律与登纳德缩放逐渐趋于停滞，进而推动计算系统通过专用硬件加速器向更大规模、更高程度的异构化与专业化发展，而非仅仅依赖工艺尺寸的微缩。实现ACES需要以越来越高的能效提供海量计算能力，因为大多数应用场景本质上受资源限制。计算性能与异构性的提升，伴随着对高内存带宽与容量的日益增长的需求，这是由于驱动应用日趋复杂，需处理海量且日益不规则的数据集，并持续需要传感器数据的实时流入，从而对片内与片外互连系统均构成更大压力。此外，ACES将实时关键任务与通用计算任务整合在同一物理平台上，共享通信、存储与微架构资源。这类异构混合关键性系统（MCSs）对互连提出了额外压力，要求不同关键级别之间的争用最小化，以维持高度的可预测性。要满足广泛工业应用中的性能与能效要求，需要对内存系统与应用场景、计算单元及加速器进行精心的协同设计。