In this paper, we present a solution to the industrial challenge put forth by ARM in 2022. We systematically analyze the effect of shared resource contention to an augmented reality head-up display (AR-HUD) case-study application of the industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano. We configure the AR-HUD application such that it can process incoming image frames in real-time at 20Hz on the platform. We use Microarchitectural Denial-of-Service (DoS) attacks as aggressor workloads of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application. This results in significant deviations of the estimated trajectories from known ground truths, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR-HUD application. To address the challenge, we propose RT-Gang++, a partitioned real-time gang scheduling framework with LLC and iGPU bandwidth throttling capabilities. By applying RT-Gang++, we are able to achieve desired level of performance of the AR-HUD application even in the presence of fully loaded aggressor tasks.

翻译：本文针对ARM公司在2022年提出的工业挑战，提出了一套解决方案。我们系统分析了异构多核平台（NVIDIA Jetson Nano）上共享资源争用对增强现实抬头显示（AR-HUD）案例应用的影响。通过配置AR-HUD应用，使其能在该平台上以20Hz实时处理输入图像帧。采用微架构拒绝服务（DoS）攻击作为挑战中的攻击负载，研究表明这些攻击可显著影响AR-HUD应用的延迟与精度。即便我们通过缓存分区和实时调度AR-HUD应用尽力减轻其影响，估算轨迹仍与已知真实值存在重大偏差。为解决该挑战，我们提出RT-Gang++——一种具备LLC与iGPU带宽限制能力的分区实时任务组调度框架。应用RT-Gang++后，即使在满载攻击任务存在的情况下，也能使AR-HUD应用达到期望的性能水平。