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1. RTMLton: An SML Runtime for Real-Time Systems

   https://doi.org/10.1007/978-3-030-39197-3_8  

   Bhargav Shivkumar, Jeffrey Murphy (January 2020, International Symposium on Practical Aspects of Declarative Languages)

   There is a growing interest in leveraging functional programming languages in real-time and embedded contexts. Functional languages are appealing as many are strictly typed, amenable to formal methods, have limited mutation, and have simple, but powerful concurrency control mechanisms. Although there have been many recent proposals for specialized domain specific languages for embedded and real-time systems, there has been relatively little progress on adapting more general purpose functional languages for programming embedded and real-time systems. In this paper we present our current work on leveraging Standard ML in the embedded and real-time domains. Specifically we detail our experiences in modifying MLton, a whole program, optimizing compiler for Standard ML, for use in such contexts. We focus primarily on the language runtime, re-working the threading subsystem and garbage collector. We provide preliminary results over a radar-based aircraft collision detector ported to SML. 

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2. Survey of Control-flow Integrity Techniques for Real-time Embedded Systems

   https://doi.org/10.1145/3538275  

   Mishra, Tanmaya; Chantem, Thidapat; Gerdes, Ryan (July 2022, ACM Transactions on Embedded Computing Systems)

   Computing systems, including real-time embedded systems, are becoming increasingly connected to allow for more advanced and safer operation. Such embedded systems are also often resource-constrained, for example, with lower processing capabilities compared to general-purpose computing systems like desktops or servers. With the advent of paradigms such as internet-of-things (IoT), embedded systems in both commercial and industrial contexts are being increasingly interconnected and exposed to the external networks to improve automation and efficiency of operation. However, allowing external interfaces to such embedded systems increases their exposure to attackers. With an increase in attacks against embedded systems ranging from home appliances to industrial control systems operating critical equipment that have real-time requirements, it is imperative that defense mechanisms be created that explicitly consider such resource and real-time constraints. Control-flow integrity (CFI) is a family of defense mechanisms that prevent attackers from modifying the flow of execution. We survey CFI techniques, ranging from the basic to state of the art, that are built for embedded systems and real-time embedded systems and find that there is a dearth, especially for real-time embedded systems, of CFI mechanisms. We then present open challenges to the community to help drive future research in this domain. 

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3. CPU Energy-Aware Parallel Real-Time Scheduling

   https://doi.org/10.4230/LIPIcs.ECRTS.2020.2  

   Saifullah, Abusayeed; Fahmida, Sezana; Modekurthy, Venkata P.; Fisher, Nathan; Guo, Zhishan (July 2020, Leibniz international proceedings in informatics)

   Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach. 

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4. Securing Real-Time Internet-of-Things

   https://doi.org/10.3390/s18124356  

   Chen, Chien-Ying; Hasan, Monowar; Mohan, Sibin (December 2018, Sensors)

   Modern embedded and cyber-physical systems are ubiquitous. Many critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality require real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However, RT-IoT are also increasingly becoming targets for cyber-attacks, which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT-IoT frameworks. 

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5. X-Stream: Accelerating streaming segments on MPSoCs for real-time applications

   https://doi.org/10.1016/j.sysarc.2023.102857  

   Tabish, Rohan; Pellizzoni, Rodolfo; Mancuso, Renato; Gracioli, Giovani; Mirosanlou, Reza; Caccamo, Marco (May 2023, Journal of Systems Architecture)

   We are witnessing a race to meet the ever-growing computation requirements of emerging AI applications to provide perception and control in autonomous vehicles — e.g., self-driving cars and UAVs. To remain competitive, vendors are packing more processing units (CPUs, programmable logic, GPUs, and hardware accelerators) into next-generation multiprocessor systems-on-a-chip (MPSoC). As a result, modern embedded platforms are achieving new heights in peak computational capacity. Unfortunately, however, the collateral and inevitable increase in complexity represents a major obstacle for the development of correct-by-design safety-critical real-time applications. Due to the ever-growing gap between fast-paced hardware evolution and comparatively slower evolution of real-time operating systems (RTOS), there is a need for real-time oriented full-platform management frameworks to complement traditional RTOS designs. In this work, we propose one such framework, namely the X-Stream framework, for the definition, synthesis, and analysis of real-time workloads targeting state-of-the-art accelerator-augmented embedded platforms. Our X-Stream framework is designed around two cardinal principles. First, computation and data movements are orchestrated to achieve predictability by design. For this purpose, iterative computation over large data chunks is divided into subsequent segments. These segments are then streamed leveraging the three-phase execution model (load, execute and unload). Second, the framework is workflow-centric: system designers can specify their workflow and the necessary code for workflow orchestration is automatically generated. In addition to automating the deployment of user-defined hardware-accelerated workloads, X-Stream supports the deployment of some computation segments on traditional CPUs. Finally, X-Stream allows the definition of real-time partitions. Each partition groups applications belonging to the same criticality level and that share the same set of hardware resources, with support for preemptive priority-driven scheduling. Conversely, freedom from interference for applications deployed in different partitions is guaranteed by design. We provide a full-system implementation that includes RTOS integration and showcase the proposed X-Stream framework on a Xilinx Ultrascale+ platform by focusing on a matrix-multiplication and addition kernel use-case. 

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