On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems

Securing Real-Time Internet-of-Things

Modern embedded and cyber-physical systems are ubiquitous. A large number of 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 requires 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

Towards a centralized multicore automotive system

Today’s automotive systems are inundated with embedded electronics to host chassis, powertrain, infotainment, advanced driver assistance systems, and other modern vehicle functions. As many as 100 embedded microcontrollers execute hundreds of millions of lines of code in a single vehicle. To control the increasing complexity in vehicle electronics and services, automakers are planning to consolidate different on-board automotive functions as software tasks on centralized multicore hardware platforms. However, these vehicle software services have different and contrasting timing, safety, and security requirements. Existing vehicle operating systems are ill-equipped to provide all the required service guarantees on a single machine. A centralized automotive system aims to tackle this by assigning software tasks to multiple criticality domains or levels according to their consequences of failures, or international safety standards like ISO 26262. This research investigates several emerging challenges in time-critical systems for a centralized multicore automotive platform and proposes a novel vehicle operating system framework to address them. This thesis first introduces an integrated vehicle management system (VMS), called DriveOS™, for a PC-class multicore hardware platform. Its separation kernel design enables temporal and spatial isolation among critical and non-critical vehicle services in different domains on the same machine. Time- and safety-critical vehicle functions are implemented in a sandboxed Real-time Operating System (OS) domain, and non-critical software is developed in a sandboxed general-purpose OS (e.g., Linux, Android) domain. To leverage the advantages of model-driven vehicle function development, DriveOS provides a multi-domain application framework in Simulink. This thesis also presents a real-time task pipeline scheduling algorithm in multiprocessors for communication between connected vehicle services with end-to-end guarantees. The benefits and performance of the overall automotive system framework are demonstrated with hardware-in-the-loop testing using real-world applications, car datasets and simulated benchmarks, and with an early-stage deployment in a production-grade luxury electric vehicle

Integrated Design Tools for Embedded Control Systems

Currently, computer-based control systems are still being implemented using the same techniques as 10 years ago. The purpose of this project is the development of a design framework, consisting of tools and libraries, which allows the designer to build high reliable heterogeneous real-time embedded systems in a very short time at a fraction of the present day costs. The ultimate focus of current research is on transformation control laws to efficient concurrent algorithms, with concerns about important non-functional real-time control systems demands, such as fault-tolerance, safety,\ud reliability, etc.\ud The approach is based on software implementation of CSP process algebra, in a modern way (pure objectoriented design in Java). Furthermore, it is intended that the tool will support the desirable system-engineering stepwise refinement design approach, relying on past research achievements ¿ the mechatronics design trajectory based on the building-blocks approach, covering all complex (mechatronics) engineering phases: physical system modeling, control law design, embedded control system implementation and real-life realization. Therefore, we expect that this project will result in an\ud adequate tool, with results applicable in a wide range of target hardware platforms, based on common (off-theshelf) distributed heterogeneous (cheap) processing units

CSP channels for CAN-bus connected embedded control systems

Closed loop control system typically contains multitude of sensors and actuators operated simultaneously. So they are parallel and distributed in its essence. But when mapping this parallelism to software, lot of obstacles concerning multithreading communication and synchronization issues arise. To overcome this problem, the CT kernel/library based on CSP algebra has been developed. This project (TES.5410) is about developing communication extension to the CT library to make it applicable in distributed systems. Since the library is tailored for control systems, properties and requirements of control systems are taken into special consideration. Applicability of existing middleware solutions is examined. A comparison of applicable fieldbus protocols is done in order to determine most suitable ones and CAN fieldbus is chosen to be first fieldbus used. Brief overview of CSP and existing CSP based libraries is given. Middleware architecture is proposed along with few novel ideas