Search for: All records

Microcontrollers (MCUs) are steadily embracing multi-core technology to meet growing performance demands. This trend marks a shift from their traditionally simple, deterministic designs to more complex and inherently less predictable architectures. While shared resource contention is well-studied in mid to high-end embedded systems, the emergence of multi-core architectures in MCUs introduces unique challenges and characteristics that existing research has not fully explored. In this paper, we conduct an in-depth investigation of both mainstream and next-generation MCU-based platforms, aiming to identify the sources of contention on systems typically lacking these problems. We empirically demonstrate substantial contention effects across different MCU architectures (i.e., from single- to multi-core configurations), highlighting significant application slowdowns. Notably, we observe that slowdowns can reach several orders of magnitude, with the most extreme cases showing up to a 3800x (times, not percent) increase in execution time. To address these issues, we propose and evaluate muTPArtc, a novel mechanism designed for Timely Progress Assessment (TPA) and TPA-based runtime control specifically tailored to MCUs. muTPArtc is an MCU-specialized TPA-based mechanism that leverages hardware facilities widely available in commercial off-the-shelf MCUs (i.e., hardware breakpoints and cycle counters) to successfully monitor applications' progress, detect, and mitigate timing violations. Our results demonstrate that muTPArtc effectively manages performance degradation due to interference, requiring only minimal modifications to the build pipeline and no changes to the source code of the target application, while incurring minor overheads. 

Abstract In today’s multiprocessor systems-on-a-chip, the shared memory subsystem is a known source of temporal interference. The problem causes logically independent cores to affect each other’s performance, leading to pessimistic worst-case execution time analysis. Memory regulation via throttling is one of the most practical techniques to mitigate interference. Traditional regulation schemes rely on a combination of timer and performance counter interrupts to be delivered and processed on the same cores running real-time workload. Unfortunately, to prevent excessive overhead, regulation can only be enforced at a millisecond-scale granularity. In this work, we present a novel regulation mechanism fromoutside the coresthat monitors performance counters for the application core’s activity in main memory at a microsecond scale. The approach is fully transparent to the applications on the cores, and can be implemented using widely available on-chip debug facilities. The presented mechanism also allows more complex composition of metrics to enact load-aware regulation. For instance, it allows redistributing unused bandwidth between cores while keeping the overall memory bandwidth of all cores below a given threshold. We implement our approach on a host of embedded platforms and conduct an in-depth evaluation on the Xilinx Zynq UltraScale+ ZCU102, NXP i.MX8M and NXP S32G2 platforms using the San Diego Vision Benchmark Suite. 

In this paper, we investigate the problem of contention and loss of predictability in modern microcontrollers (MCU). To address this issue, we first present a framework to empirically analyze and observe the impact of interference on low-end MCUs. With carefully crafted evaluation scenarios, we conduct experiments on an Arm’s Musca-A1 platform and provide sufficient evidence that even with common application setups, interference can slowdown applications by several orders of magnitude. Furthermore, we propose an architecture for a novel mitigation system that enables applications to monitor their timing progress slackness and mitigate temporal interference over shared resources. This is achieved by suspending less critical cores and reconfiguring their priority on the bus when intolerable contention delays are present. Our findings emphasize the critical importance of considering the impact of shared resources, such as interconnects and memory access patterns, on low-end multi-core MCUs. It is, therefore, crucial to design mechanisms that can allow MCU-based applications to regain control of their timeliness. 

In today’s multiprocessor systems-on-a-chip (MPSoC), the shared memory subsystem is a known source of temporal interference. The problem causes logically independent cores to affect each other’s performance, leading to pessimistic worst-case execution time (WCET) analysis. One of the most practical techniques to mitigate interference is memory regulation via throttling. Traditional regulation schemes rely on a combination of timer and performance counter interrupts to be delivered and processed on the same cores running real-time workload. Unfortunately, to prevent excessive overhead, regulation can only be enforced at a millisecond-scale granularity. In this work, we present a novel regulation mechanism from outside the cores that monitors performance counters for the application core’s activity in main memory at a microsecond scale. The approach is fully transparent to the applications on the cores, and can be implemented using widely available on-chip debug facilities. The presented mechanism also allows a more complex composition of metrics to enact load-aware regulation. For instance, it allows redistributing unused bandwidth between cores while keeping the overall memory bandwidth of all cores below a given threshold. We implement our approach on a host of embedded platforms and carry out an in-depth evaluation on the Xilinx Zynq UltraScale+ ZCU102 platform using the SD-VBS. 

The correctness of safety-critical systems depends on both their logical and temporal behavior. Control-flow integrity (CFI) is a well-established and understood technique to safeguard the logical flow of safety-critical applications. But unfortunately, no established methodologies exist for the complementary problem of detecting violations of control flow timeliness. Worse yet, the latter dimension, which we term Timely Progress Integrity (TPI), is increasingly more jeopardized as the complexity of our embedded systems continues to soar. As key resources of the memory hierarchy become shared by several CPUs and accelerators, they become hard-to-analyze performance bottlenecks. And the precise interplay between software and hardware components becomes hard to predict and reason about. How to restore control over timely progress integrity? We postulate that the first stepping stone toward TPI is to develop methodologies for Timely Progress Assessment (TPA). TPA refers to the ability of a system to live-monitor the positive/negative slack - with respect to a known reference - at key milestones throughout an application’s lifespan. In this paper, we propose one such methodology that goes under the name of Milestone-Based Timely Progress Assessment or MB-TPA, for short. Among the key design principles of MB-TPA is the ability to operate on black-box binary executables with near-zero time overhead and implementable on commercial platforms. To prove its feasibility and effectiveness, we propose and evaluate a full-stack implementation called Timely Progress Assessment with 0 Overhead (TPAw0v). We demonstrate its capability in providing live TPA for complex vision applications while introducing less than 0.6% time overhead for applications under test. Finally, we demonstrate one use case where TPA information is used to restore TPI in the presence of temporal interference over shared memory resources.