Anna Minaeva, who recently received her PhD degree, just had a journal article entitled “Control Performance Optimization for Application Integration on Automotive Architectures” accepted for publication in IEEE Transactions on Computers. This article is the result of a HiPEAC collaboration grant that Anna was awarded back in 2016 to visit the group of Samarjit Chakraborty at TU Munich. I am very happy to see that this grant resulted in a joint publication in a prestigious journal and hope to collaborate with Samarjit again in the future.
The article addresses the problem of generating a time-triggered schedule for a number of independently developed automotive applications on a number of shared resources, such that their control performance only suffers minimal degradation. The three main contributions are: 1) a constraint programming model that solves the problem optimally, exploiting properties of the problem to reduce the computation time; 2) a fast heuristic called Flexi that only has a minor impact on the optimality of the solution; and 3) an experimental evaluation of the scalability and efficiency of the proposed approaches on a case study, in addition to several synthetic datasets. The results show that the heuristic provides a solution on average 5 times faster, finding a feasible solution in 31% more problem instances than the optimal approach within a time limit, while only sacrificing 0.5% of the control performance quality for the largest dataset.
Anna Minaeva had an article entitled “Time-Triggered Co-Scheduling of Computation and Communication with Jitter Requirements” accepted in IEEE Transactions on Computers. The article considers the problem of efficiently co-scheduling task execution and communication in multi-core automotive platforms. Most existing works typically deal with zero-jitter scheduling, which results in lower resource utilization, but has lower memory requirements. In contrast, this article focuses on jitter-constrained scheduling that puts constraints on the tasks jitter, increasing schedulability over zero-jitter scheduling.
The contributions of this article are: 1) Integer Linear Programming and Satisfiability Modulo Theory model exploiting problem-specific information to reduce the formulations complexity to schedule small applications. 2) A heuristic approach, employing three levels of scheduling scaling to real-world use-cases with 10000 tasks and messages. 3) An experimental evaluation of the proposed approaches on a case-study and on synthetic data sets showing the efficiency of both zero-jitter and jitter-constrained scheduling. It shows that up to 28% higher resource utilization can be achieved by having up to 10 times longer computation time with relaxed jitter requirements.
Manil Dev Gomony just had a journal article entitled “A Globally Arbitrated Memory Tree for Mixed-Time-Criticality Systems” accepted in the high-impact journal IEEE Transactions on Computers. This article extends a conference paper published at DATE in 2015 that was called “A Generic, Scalable and Globally Arbitrated Memory Tree for Shared DRAM Access in Real-Time Systems” that was published in collaboration with Jamie Garside and Neil Audsley from University of York. The original paper explained the design and efficient hardware implementation of a transaction arbiter for real-time systems that could be configured to behave like any of five well-known arbiters, i.e. TDM, Round Robin, Credit-Controlled Static Priority, Priority-Based Scheduler, and Frame-Based Static Priority. The key feature of the arbiter is that it is distributed, which means that accounting and enforcement is not done in a single centralized location, allowing it to scale to systems with many resource clients without negatively impacting the maximum frequency at which it operates.
The journal article extends the original conference paper by adding more detail and examples on the design of the memory tree, as well as improving positioning. However, it also extends the scope of the work to consider more complex Mixed-Time-Criticality systems where some clients are more concerned about average-case than worst-case performance. It also considers that the requirements of the clients may be diverse, i.e. that some may have high bandwidth requirements and are latency-tolerant, while others have low bandwidth requirements, but are latency-critical. This is diversity of requirements is addressed by showing how the memory tree supports the transaction arbiter to be chosen individually per client rather than once for the entire system. For example, some real-time clients may be configured by non-work-conserving TDM arbitration to get predictable bandwidth and latency while enjoying complete temporal isolation from other clients, which simplifies integration and certification. Other clients sharing the same resource, may be scheduled using e.g. using a work-conserving Frame-Based Static Priority scheduler to reflect an interest in low average latency while still distinguishing their relative latency-sensitivity. The memory tree supports any combination of the mechanisms discussed above, but we provide a formal analysis of the mixed arbitration algorithm explained above. The article demonstrates the benefits of this approach on a VHDL hardware implementation, as well as its cost in terms of area and power compared to centralized non-mixed arbitration policies by means of ASIC synthesis.
The spree of accepted journal articles continues as Sven Goossens’ article entitled “Power/Performance Trade-offs in Real-Time SDRAM Command Scheduling” was accepted for publication in IEEE Transactions on Computers. The article contains a detailed discussion about the trade-offs between bandwidth, execution time, and power when DRAM requests are scheduled by a real-time memory controller under a close-page policy. The results cover a wide range of memories ranging from DDR2/3/4 to LPDDR1/2/3 for different request sizes and amounts of bank parallelism. Other key contributions of the article are: 1) publicly available heuristic and optimal algorithms for generation of memory patters that covers all aforementioned memories, 2) a simple abstraction that quickly captures the differences between the different DRAM generations allowing algorithms and analyses to be easily adapted to cover all of them, and 3) a pairwise bank-group interleaving scheme for DDR4 that exploits bank grouping for improved performance.