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We propose a server-based approach to manage a general-purpose graphics processing unit (GPU) in a predictable and efficient manner. Our proposed approach introduces a GPU server that is a dedicated task to handle GPU requests from other tasks on their behalf. The GPU server ensures bounded time to access the GPU, and allows other tasks to suspend during their GPU computation to save CPU cycles. By doing so, we address the two major limitations of the existing real-time synchronization-based GPU management approach: busy waiting and long priority inversion. We have implemented a prototype of the server-based approach on a real embedded platform. This case study demonstrates the practicality and effectiveness of the server-based approach. Experimental results indicate that the server-based approach yields significant improvements in task schedulability over the existing synchronization-based approach in most practical settings. Although we focus on a GPU in this paper, the server-based approach can also be used for other types of computational accelerators.

In this report, we apply an input-output transformation passivation method, described in our previous works, to an Adaptive Cruise Control system. We analyze the system's performance under a co-simulation framework that makes use of an online optimization method called extremum-seeking to achieve the optimized behavior. The matrix for passivation method encompasses commonly used methods of series, feedback and feed-forward interconnections for passivating the system. We have previously shown that passivity levels can be guaranteed for a system using our passivation method. In this work, an extremum-seeking algorithm was used to determine the passivation parameters. It is known that systems with input-output time-delays are not passive. On the other hand, time-delays are unavoidable in automotive systems and commonly emerge in software implementations and communication units as well as driver's behavior. We show that by using our passivation method, we can passivate the system and improve its overall performance. Our simulation examples in CarSim and Simulink will show that the passive system has a considerably better performance., Comment: 39 pages, 18 figures, Technical Report at the University of Notre Dame, American Control Conference (ACC), 2016

Modern cyber-physical systems (CPS) have a close inter-dependence between software and physical components. Automotive embedded systems are typical CPS, as physical chips, sensors and actuators are physical components and software embedded within are the cyber components. The current stage of embedded systems is highly complex in architecture design for both software and hardware. It is common in industrial practice that high level control algorithm development and low level code implementation on hardware platforms are developed separately with limited shared information. However, software code and hardware architecture become closely related with the increasing complexity. Correlated requirements and dependencies between hardware and software are emerging problems of industrial practice. We demonstrate in this paper a method to link model based system design with real-time simulations and analysis of the architecture model. This allows hardware software co-design and thus early selection of hardware architecture., Comment: AVICPS 2014