Process-variation aware mapping of real-time streaming applications to MPSoCs for improved yield

Abstract—As technology scales, the impact of process variation on the maximum supported frequency (FMAX) of individual cores in a MPSoC becomes more pronounced. Task allocation without variation-aware performance analysis can result in a significant loss in yield, defined as the number of manufactured chips satisfying the application timing requirement. We propose variation-aware task allocation for real-time streaming applica-tions modeled as task graphs. Our solutions are primarily based on the throughput requirement, which is the most important timing requirement in many real-time streaming applications. The three main contributions of this paper are: 1) Using data flow graphs that are well-suited for modeling and analysis of real-time streaming applications, we explicitly model task execution both in terms of clock cycles (which is independent of variation) and seconds (which does depend on the variation of the resource), which we connect by an explicit binding. 2) We present two approaches for optimizing the yield. The approaches give different results at different costs. 3) We present exhaustive and heuristic algorithms that implement the optimization approaches. Our variation-aware mapping algorithms are tested on models of real applications, and are compared to the mapping methods that are unaware of hardware variation. Our results demonstrate yield improvements of up to 50 % with an average of 31%, showing the effectiveness of our approaches. Index Terms—Process variation, Multiprocessor System-on

Mixed-Criticality Scheduling with Dynamic Redistribution of Shared Cache

The design of mixed-criticality systems often involves painful tradeoffs between safety guarantees and performance. However, the use of more detailed architectural models in the design and analysis of scheduling arrangements for mixed-criticality systems can provide greater confidence in the analysis, but also opportunities for better performance. Motivated by this view, we propose an extension of Vestal\u27s model for mixed-criticality multicore systems that (i) accounts for the per-task partitioning of the last-level cache and (ii) supports the dynamic reassignment, for better schedulability, of cache portions initially reserved for lower-criticality tasks to the higher-criticality tasks, when the system switches to high-criticality mode. To this model, we apply partitioned EDF scheduling with Ekberg and Yi\u27s deadline-scaling technique. Our schedulability analysis and scalefactor calculation is cognisant of the cache resources assigned to each task, by using WCET estimates that take into account these resources. It is hence able to leverage the dynamic reconfiguration of the cache partitioning, at mode change, for better performance, in terms of provable schedulability. We also propose heuristics for partitioning the cache in low- and high-criticality mode, that promote schedulability. Our experiments with synthetic task sets, indicate tangible improvements in schedulability compared to a baseline cache-aware arrangement where there is no redistribution of cache resources from low- to high-criticality tasks in the event of a mode change

An Empirical Survey-based Study into Industry Practice in Real-time Systems

This paper presents results and observations from a survey of 120 industry practitioners in the field of real-time embedded systems. The survey provides insights into the characteristics of the systems being developed today and identifies important trends for the future. The survey aims to inform both academics and practitioners, helping to avoid divergence between industry practice and fundamental academic research

Worst-case Stall Analysis for Multicore Architectures with Two Memory Controllers

In multicore architectures, there is potential for contention between cores when accessing shared resources, such as system memory. Such contention scenarios are challenging to accurately analyse, from a worst-case timing perspective. One way of making memory contention in multicores more amenable to timing analysis is the use of memory regulation mechanisms. It restricts the number of accesses performed by any given core over time by using periodically replenished per-core budgets. Typically, this assumes that all cores access memory via a single shared memory controller. However, ever-increasing bandwidth requirements have brought about architectures with multiple memory controllers. These control accesses to different memory regions and are potentially shared among all cores. While this presents an opportunity to satisfy bandwidth requirements, existing analysis designed for a single memory controller are no longer safe. This work formulates a worst-case memory stall analysis for a memory-regulated multicore with two memory controllers. This stall analysis can be integrated into the schedulability analysis of systems under fixed-priority partitioned scheduling. Five heuristics for assigning tasks and memory budgets to cores in a stall-cognisant manner are also proposed. We experimentally quantify the cost in terms of extra stall for letting all cores benefit from the memory space offered by both controllers, and also evaluate the five heuristics for different system characteristics

A Comprehensive Survey of Industry Practice in Real-time Systems

This paper presents results and observations from a survey of 120 industry practitioners in the field of real-time embedded systems. The survey provides insights into the characteristics of the systems being developed today and identifies important trends for the future. It extends the results from the survey data to the broader population that it is representative of, and discusses significant differences between application domains. The survey aims to inform both academics and practitioners, helping to avoid divergence between industry practice and academic research. The value of this research is highlighted by a study showing that the aggregate findings of the survey are not common knowledge in the real-time systems community

Predator: A Predictable SDRAM Memory Controller ABSTRACT

Memory requirements of intellectual property components (IP) in contemporary multi-processor systems-on-chip are increasing. Large high-speed external memories, such as DDR2 SDRAMs, are shared between a multitude of IPs to satisfy these requirements at a low cost per bit. However, SDRAMs have highly variable access times that depend on previous requests. This makes it difficult to accurately and analytically determine latencies and the useful bandwidth at design time, and hence to guarantee that hard real-time requirements are met. The main contribution of this paper is a memory controller design that provides a guaranteed minimum bandwidth and a maximum latency bound to the IPs. This is accomplished using a novel two-step approach to predictable SDRAM sharing. First, we define memory access groups, corresponding to precomputed sequences of SDRAM commands, with known efficiency and latency. Second, a predictable arbiter is used to schedule these groups dynamically at run-time, such that an allocated bandwidth and a maximum latency bound is guaranteed to the IPs. The approach is general and covers all generations of SDRAM. We present a modular implementation of our memory controller that is efficiently integrated into the network interface of a network-on-chip. The area of the implementation is cheap, and scales linearly with the number of IPs. An instance with six ports runs at 200 MHz and requires 0.042 mm 2 in 0.13µm CMOS technology

Architectures and Modeling of Predictable Memory Controllers for Improved System Integration

Abstract—Designing multi-processor systems-on-chips becomes increasingly complex, as more applications with realtime requirements execute in parallel. System resources, such as memories, are shared between applications to reduce cost, causing their timing behavior to become inter-dependent. Using conventional simulation-based verification, this requires all concurrently executing applications to be verified together, resulting in a rapidly increasing verification complexity. Predictable and composable systems have been proposed to address this problem. Predictable systems provide bounds on performance, enabling formal analysis to be used as an alternative to simulation. Composable systems isolate applications, enabling them to be verified independently. Predictable and composable systems are built from predictable and composable resources. This paper presents three general techniques to implement and model predictable and composable resources, and demonstrates their applicability in the context of a memory controller. The architecture of the memory controller is general and supports both SRAM and DDR2/DDR3 SDRAM and a wide range of arbiters, making it suitable for many predictable and composable systems. The modeling approach is based on a shared-resource abstraction that covers any combination of supported memory and arbiter and enables system-level performance analysis with a variety of well-known frameworks, such as network calculus or data-flow analysis. Index Terms—predictability; composability; memory controller; memory patterns; real-time; SDRAM; arbitration; latency-rate servers I