Hardware Architectures For Secure, Reliable, And Energy-Efficient Real-Time Systems

The last decade has seen an increased ubiquity of computers with the widespread adoption of smartphones and tablets and the continued spread of embedded cyber-physical systems. With this integration into our environments, it has become important to consider the real-world interactions of these computers rather than simply treating them as abstract computing machines. For example, cyber-physical systems typically have real-time constraints in order to ensure safe and correct physical interactions. Similarly, even mobile and desktop systems, which are not traditionally considered real-time systems, are inherently time-sensitive because of their interaction with users. Traditional techniques proposed for improving hardware architectures are not designed with these real-time constraints in mind. In this thesis, we explore some of the challenges and opportunities in hardware design for real-time systems. Specifically, we study recent techniques for improving security, reliability, and energyefficiency of computer systems. Run-time monitoring has been shown to be a promising technique for improving system security and reliability. Applying run-time monitoring to realtime systems introduces challenges due to the performance impact of monitoring. To address this, we first developed a technique for estimating the worstcase execution time impact of run-time monitoring, enabling it to be applied within existing real-time system design methodologies. Next, we developed hardware architectures that selectively enable and disable monitoring in order to meet real-time deadlines while still allowing a portion of the monitoring to be performed. We show architectures for hard and soft real-time systems, with different design decisions and trade-offs for each. Dynamic voltage and frequency scaling (DVFS) is commonly found in modern processors as a way to dynamically trade off power and performance. In the presence of real-time deadlines, this presents an opportunity for improved energy-efficiency by slowing down jobs to reduce energy while still meeting deadline requirements. We developed a method for creating DVFS controllers that are able to predict the appropriate frequency level for tasks before they execute and show improved energy savings and reduced deadline misses compared to current state-of-the-art DVFS controllers