Silicon Lifecycle Management (SLM) has emerged as a strategic solution to address the challenges on meeting increasing silicon production quality and in-field dependability requirements. In this Innovation Practices track, we invited industry experts to discuss the advances in silicon lifecycle reliability, safety and security domains, respectively.

Assessing the reliability of modern devices running CNN algorithms is a very difficult task. Actually, the complexity of the state-of-the-art devices makes exhaustive Fault Injection (FI) campaigns impractical and typically out of the computational capabilities. A possible solution consists of resorting to statistical FI campaigns that allow a reduction in the number of needed experiments by injecting only a carefully selected small part of it. Under specific hypothesis, statistical FIs guarantee an accurate picture of the problem, albeit selecting a reduced sample size. The main problems today are related to the choice of the sample size, the location of the faults, and the correct understanding of the statistical assumptions. The intent of this paper is twofold: first, we describe how to correctly specify statistical FIs for Convolutional Neural Networks; second, we propose a data analysis on the CNN parameters that drastically reduces the number of FIs needed to achieve statistically significant results without compromising the validity of the proposed method. The methodology is experimentally validated on two CNNs, ResNet-20 and MobileNetV2, and the results show that a statistical FI campaign on about 1.21% and 0.55% of the possible faults, provides very precise information of the CNN reliability. The statistical results have been confirmed by the exhaustive FI campaigns on the same cases of study.

The widespread use of artificial intelligence (AI)-based systems has raised several concerns about their deployment in safety-critical systems. Industry standards, such as ISO26262 for automotive, require detecting hardware faults during the mission of the device. Similarly, new standards are being released concerning the functional safety of AI systems (e.g., ISO/IEC CD TR 5469). Hardware solutions have been proposed for the infield testing of the hardware executing AI applications; however, when used in applications such as Convolutional Neural Networks (CNNs) in image processing tasks, their usage may increase the hardware cost and affect the application performances. In this paper, for the very first time, a methodology to develop high-quality test images, to be interleaved with the normal inference process of the CNN application is proposed. An Image Test Library (ITL) is developed targeting the on-line test of GPU functional units. The proposed approach does not require changing the actual CNN (thus incurring in costly memory loading operations) since it is able to exploit the actual CNN structure. Experimental results show that a 6-image ITL is able to achieve about 95% of stuck-at test coverage on the floating-point multipliers in a GPU. The obtained ITL requires a very low test application time, as well as a very low memory space for storing the test images and the golden test responses.

This paper encompasses three contributions by industry professionals and university researchers. The contributions describe different trends in automotive products, including both manufacturing test and run-time reliability strategies. The subjects considered in this session deal with critical factors, from optimizing the final test before shipment to market to in-field reliability during operative life.

Since the early 1990s, federated avionics architecture - where one computing resource executes only one application, is being replaced by Integrated Modular Avionics (IMA) architectures. IMA architectures employ a partitioned environment that hosts multiple avionics functions of different safety criticalities on a common computing platform. This provides for size, weight, and power savings via denser functional integration. Several cores integrated onto one device allows more functions to be integrated together on one processor and in one piece of equipment. The use of multicore processors in safety-critical avionics applications will provide growth for further integration for the future generations of these systems. Hence aerospace equipment suppliers are interested in using Multi-Core Processors (MCPs) in their systems. With the rapid increase in demand for computational performance and cost optimum, Single-Core Processors (SCPs) are likely to become obsolete. However, with the shift to multi-core processors, compliance to safety requirements is becoming critical. The development and use of increasingly complex electronic hardware by the aviation industry for more of the safety-critical aircraft functions is creating new safety and certification concerns.