Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-critical systems in which Machine Learning (ML) algorithms learn optimized and safe solutions. AI can also support and assist human safety engineers in developing safety-critical systems. However, reconciling both cutting-edge and state-of-the-art AI technology with safety engineering processes and safety standards is an open challenge that must be addressed before AI can be fully embraced in safety-critical systems. Many works already address this challenge, resulting in a vast and fragmented literature. Focusing on the industrial and transportation domains, this survey structures and analyzes challenges, techniques, and methods for developing AI-based safety-critical systems, from traditional functional safety systems to autonomous systems. AI trustworthiness spans several dimensions, such as engineering, ethics and legal, and this survey focuses on the safety engineering dimension.

The rigorous application of static timing analysis requires a large and costly amount of detail knowledge on the hardware and software components of the system. Probabilistic Timing Analysis has potential for reducing the weight of that demand. In this paper, we present a sound measurement-based probabilistic timing analysis technique based on Extreme Value Theory. In all the experiments made as part of this work, the timing bounds determined by our technique were less than 15% pessimistic in comparison with the tightest possible bounds obtainable with any probabilistic timing analysis technique. As a point of interest to industrial users, our technique also requires a comparatively low number of measurement runs of the program under analysis, less than 650 runs were needed for the benchmarks presented in this paper.

Transistors consist of lower number of atoms with every technology generation. Such atoms may be displaced due to the stress caused by high temperature, frequency and current, leading to failures. NBTI (negative bias temperature instability) is one of the most important sources of failure affecting transistors. NBTI degrades PMOS transistors whenever the voltage at the gate is negative (logic input "0"). The main consequence is a reduction in the maximum operating frequency and an increase in the minimum supply voltage of storage structures to cope for the degradation. Many PMOS transistors affected by NBTI can be found in both combinational and storage blocks since they observe a "0 " at their gates most of the time. This paper proposes and evaluates the design of Penelope, an NBTI-aware processor. We propose (i) generic strategies to mitigate degradation in both combinational and storage blocks, (ii) specific techniques to protect individual blocks by applying the global strategies, and (Hi) a metric to assess the benefits of reduced degradation and the overheads in performance and power.

Static timing analysis is the state-of-the-art practice of ascertaining the timing behavior of current-generation real-time embedded systems. The adoption of more complex hardware to respond to the increasing demand for computing power in next-generation systems exacerbates some of the limitations of static timing analysis. In particular, the effort of acquiring (1) detailed information on the hardware to develop an accurate model of its execution latency as well as (2) knowledge of the timing behavior of the program in the presence of varying hardware conditions, such as those dependent on the history of previously executed instructions. We call these problems the timing analysis walls. In this vision-statement article, we present probabilistic timing analysis , a novel approach to the analysis of the timing behavior of next-generation real-time embedded systems. We show how probabilistic timing analysis attacks the timing analysis walls; we then illustrate the mathematical foundations on which this method is based and the challenges we face in the effort of efficiently implementing it. We also present experimental evidence that shows how probabilistic timing analysis reduces the extent of knowledge about the execution platform required to produce probabilistically accurate WCET estimations.

In the last three decades a number of methods have been devised to find upper-bounds for the execution time of critical tasks in time-critical systems. Most of such methods aim to compute Worst-Case Execution Time (WCET) estimates, which can be used as trustworthy upper-bounds for the execution time that the analysed programs will ever take during operation. The range of analysis approaches used include static, measurement-based and probabilistic methods, as well as hybrid combinations of them. Each of those approaches delivers its results on the assumption that certain hypotheses hold on the timing behaviour of the system as well that the user is able to provide the needed input information. Often enough the trustworthiness of those methods is only adjudged on the basis of the soundness of the method itself. However, trustworthiness rests a great deal also on the viability of the assumptions that the method makes on the system and on the user's ability, and on the extent to which those assumptions hold in practice. This paper discusses the hypotheses on which the major state-of-the-art timing analyses methods rely, identifying pitfalls and challenges that cause uncertainty and reduce confidence on the computed WCET estimates. While identifying weaknesses, this paper does not wish to discredit any method but rather to increase awareness on their limitations and enable an informed selection of the technique that best fits the user needs.

As technology evolves, power dissipation increases and cooling systems become more complex and expensive. There are two main sources of power dissipation in a processor: dynamic power and leakage. Dynamic power has been the most significant factor, but leakage will become increasingly significant in future. It is predicted that leakage will shortly be the most significant cost as it grows at about a 5× rate per generation. Thus, reducing leakage is essential for future processor design. Since large caches occupy most of the area, they are one of the leakiest structures in the chip and hence, a main source of energy consumption for future processors.This paper introduces IATAC (inter-access time per access count), a new hardware technique to reduce cache leakage for L2 caches. IATAC dynamically adapts the cache size to the program requirements turning off cache lines whose content is not likely to be reused. Our evaluation shows that this approach outperforms all previous state-of-the-art techniques. IATAC turns off 65% of the cache lines across different L2 cache configurations with a very small performance degradation of around 2%.