The increasing complexity of safety-critical hardware systems demands advanced methods for ensuring functional safety (FuSa). Traditional techniques like ATPG and BIST are intrusive, requiring additional hardware and disrupting operations, making them unsuitable for in-field testing. To address this, for the first time, we propose a machine learning (ML)-driven automated Self-Test Library (STL) generation for seamless in-field testing during idle periods, ensuring uninterrupted fault detection and high system performance. Utilizing reinforcement learning, the STL generates design-specific test patterns, achieving up to $57.57 \%$ improvement in fault coverage and up to $85 \%$ efficiency compared to existing pattern-based testing, enhancing FuSa in mission-critical applications.

Assertions are critical components used in hardware verification, ensuring robust functionality, fortifying design security, and providing essential verification features. Traditional hardware assertion methods are not automated, complicate security audits, and require effort, causing prolonged development cycles. Recent studies have highlighted the potential of commercial Large Language Models (LLMs) to generate security-focused assertions by leveraging textual data from design specifications. However, reliance on proprietary models like GPT-4 severely jeopardizes IP privacy and data confidentiality, undermining transparency and accountability in data handling practices. In this paper, we address secure hardware assertion generation by proposing a practical approach to significantly enhance the feasibility of open-source LLMs. Our proposed method, OpenAssert, involves fine-tuning existing models to be utilized locally at the user’s end without compromising confidentiality. Additionally, we employ Retrieval Augmentation Generation to refine these models, mitigating hallucinations and security-related errors. OpenAssert demonstrates improvements, achieving up to a 44% increase in rouge-1 score, a 49% improvement in cosine similarity, and a 43.4% reduction in word error rate for security-critical designs compared to open-source models.

The widespread adoption of Deep Neural Networks (DNNs) can be attributed to their remarkable performance in tackling complex real-world problems. Consequently, they have found extensive use in everyday applications as well as in high-assurance environments. Nonetheless, various challenges undermine the reliability of these DNNs in mission-critical scenarios. One such challenge is circuit aging, an inevitable consequence of prolonged usage leading to the deterioration of circuit performance. Therefore, it is of utmost importance to grasp the implications of circuit aging at the application level and to adopt proactive strategies for mitigating these effects. Towards this end, our paper examines the adverse effects of circuit aging on the performance of DNN applications and introduce a novel aging-aware training (AAT) framework to mitigate such detrimental impacts. To the best of our knowledge, this framework is the first of its kind, expressly tailored to train models while considering the impact of aging. Additionally, to extend the operational lifespan of the system, as opposed to its immediate disposal, we advocate a strategic model replacement approach based on a performance threshold, particularly when aging becomes a prominent concern. Through extensive experiments involving cutting-edge DNN models, we observe substantial performance enhancements of up to 78% when utilizing AAT, even in the presence of aging, as compared to training without AAT. The model replacement approach yields significant results as well, exhibiting up to 30% relative improvement in accuracy when subjected to the same application workload. Furthermore, this improvement is augmented with AAT, achieving an additional 20% improvement, demonstrating the efficacy of the proposed framework.

What are the outcomes of using a local energy market (LEM) to trade electricity between participants, retailers/suppliers and the network operator? Such a question is becoming increasingly important for electrical grids as more and more solar photovoltaics (PVs) and battery energy storage systems (BESS) are introduced. This paper presents the formulation and economic analysis of a peer-to-peer (P2P)-driven LEM to determine its suitability for each of the players in the market. To do so, a framework is proposed to define the objective function of the LEM while the financial and network parameters are considered. Then, the designed model is deployed on an actual Australian suburb containing 300 participants — 200 consumers, 50 prosumers with solar PVs, and 50 prosumers with solar PVs and BESSs. This research examines the case of two retailers/suppliers and the network operator to evaluate the financial gains which are compared to the business-as-usual (BAU), where consumers buy electricity from the grid while prosumers sell excess energy back to the grid, via feed-in-tariff (FiT) mechanism. The simulation results emphasise that with a LEM: 1) all participants save money, with prosumers owning solar PVs and BESSs gaining the most; 2) the income margin of the retailer with only consumers remains unaffected, but it is slightly increased for other retailer with prosumers; and 3) the network operator sees a slight increase in its income and grid congestion will reduce.