Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Analyses and optimizations of timing-constrained embedded systems considering resource synchronization and machine learning approaches

Nowadays, embedded systems have become ubiquitous, powering a vast array of applications from consumer electronics to industrial automation. Concurrently, statistical and machine learning algorithms are being increasingly adopted across various application domains, such as medical diagnosis, autonomous driving, and environmental analysis, offering sophisticated data analysis and decision-making capabilities. As the demand for intelligent and time-sensitive applications continues to surge, accompanied by growing concerns regarding data privacy, the deployment of machine learning models on embedded devices has emerged as an indispensable requirement. However, this integration introduces both significant opportunities for performance enhancement and complex challenges in deployment optimization. On the one hand, deploying machine learning models on embedded systems with limited computational capacity, power budgets, and stringent timing requirements necessitates additional adjustments to ensure optimal performance and meet the imposed timing constraints. On the other hand, the inherent capabilities of machine learning, such as self-adaptation during runtime, prove invaluable in addressing challenges encountered in embedded systems, aiding in optimization and decision-making processes. This dissertation introduces two primary modifications for the analyses and optimizations of timing-constrained embedded systems. For one thing, it addresses the relatively long access times required for shared resources of machine learning tasks. For another, it considers the limited communication resources and data privacy concerns in distributed embedded systems when deploying machine learning models. Additionally, this work provides a use case that employs a machine learning method to tackle challenges specific to embedded systems. By addressing these key aspects, this dissertation contributes to the analysis and optimization of timing-constrained embedded systems, considering resource synchronization and machine learning models to enable improved performance and efficiency in real-time applications with stringent constraints

Embracing Visual Experience and Data Knowledge: Efficient Embedded Memory Design for Big Videos and Deep Learning

Energy efficient memory designs are becoming increasingly important, especially for applications related to mobile video technology and machine learning. The growing popularity of smart phones, tablets and other mobile devices has created an exponential demand for video applications in today?s society. When mobile devices display video, the embedded video memory within the device consumes a large amount of the total system power. This issue has created the need to introduce power-quality tradeoff techniques for enabling good quality video output, while simultaneously enabling power consumption reduction. Similarly, power efficiency issues have arisen within the area of machine learning, especially with applications requiring large and fast computation, such as neural networks. Using the accumulated data knowledge from various machine learning applications, there is now the potential to create more intelligent memory with the capability for optimized trade-off between energy efficiency, area overhead, and classification accuracy on the learning systems. In this dissertation, a review of recently completed works involving video and machine learning memories will be covered. Based on the collected results from a variety of different methods, including: subjective trials, discovered data-mining patterns, software simulations, and hardware power and performance tests, the presented memories provide novel ways to significantly enhance power efficiency for future memory devices. An overview of related works, especially the relevant state-of-the-art research, will be referenced for comparison in order to produce memory design methodologies that exhibit optimal quality, low implementation overhead, and maximum power efficiency.National Science FoundationND EPSCoRCenter for Computationally Assisted Science and Technology (CCAST