№ 01 (2025) Volume 05 Issue 01

Managing communication between microservices heavily depends on scalability and automation in modern cloud-native systems. Using Mustache gives large-scale applications a dependable way to add personalized sections to their email templates. This report explores how Mustache is used in cloud environments, mainly focusing on how it works with microservices, automation through AI, and DevOps procedures. Separating the organization’s logic from its look allows developers and product teams to function together with fewer errors, promoting efficient updates and dependability. This part of the paper discusses email template management strategies, including version control, template management, and context provided by different microservices. It also proves that pairing Mustache with technologies like Kubernetes, event-driven designs, and CI/CD methods plays a key role in designing systems that scale easily and adapt to change. Also, applications of advanced AI, which personalize content and deliver updates during events, highlight how Mustache can help improve customer interaction and company updates. With this strategy, businesses can deal with the problems of building scalable and adaptable systems for messaging and, at the same time, ensure all communications are secure and flexible on different platforms. In the conclusion, the paper discusses future options involving using serverless systems and testing with machine learning, making Mustache-based frameworks faster and more flexible.

In a modern-day digital economy, computational requirements for high-stakes industries such as finance, real estate, retail, and cloud computing must be met by Graphics Processing Units (GPUs). Reliability and performance of such GPUs are integral, as small failures can cause large-scale business disruptions and financial losses. This paper examines the architectural and methodological models for designing a fault-tolerant test infrastructure in the large-scale production of GPUs. It highlights the requirement of redundancy, modularity, real-time monitoring, and automated error check prototyping for keeping throughput and reliability at the industrial level. By presenting a detailed analysis of sector-specific utilization, the study shows how GPUs fuel critical missions such as high-frequency trading, immersive real estate model creation, and real-time recommendation engines in e-commerce. A robust testing architecture is illustrated, including modular test cells, cloud-integrated environments, and an intelligent diagnostic system that can manage thermal, voltage, and computational faults. The methodology section describes data-driven test strategies, edge case simulations, and proposals for continuous integrated pipelines. Accenture’s successful case study exemplifies how an AI-powered fault-tolerant testing grid can achieve real-world success by reducing post-deployment failures by 42%. Predictive maintenance and multi-level monitoring methods are also described as requirements for scalable, resilient infrastructure. The study ends with the future trends of self-healing environments, AI-driven root cause analysis, and sustainable testing practices. This framework provides a technical and strategic roadmap for manufacturers that plan to provide the same level of GPU performance in the face of the ever-increasing requirements of AI-centric, real-time, and cloud-based applications.

With the architecture complexity of silicon in high-performance computing (HPC) and graphics processing units (GPUs) growing, reliability, scalability, and first-time-right silicon cannot be achieved without the introduction of advanced Design for Test (DFT) methodologies. This paper addresses the peculiarities of DFT magnetization to cope with the characteristics of HPC and GPU environment issues: massive parallelism, depth pipelining, multi-clock, power domains, and rising thermal and power density. It covers basic techniques, including scan-based testing, built-in self-test (BIST), logic BIST (LBIST), and a modular and hierarchical test planning framework. Additionally, the paper studies the related key infrastructural pieces, such as test access mechanisms (IJTAG, IEEE 1500), remote debug orchestration, and centralized test control units. Additionally, emerging trends like AI/ML-enabled ATPG, in-field telemetry, predictive maintenance, and DFT innovations in the contexts of chipset-based and 3D-integrated architecture alter the test requirements for the overall multi-die system. It provides best practices in early DFT planning, modular IP reuse, scan chain optimization, and power-aware test pattern generation to obtain high test coverage while maintaining silicon performance. This work presents actionable insights for high-yield silicon design and validation in the next-generation compute platform landscape. It is aimed at silicon architects, DFT engineers, and verification professionals.

Electromagnetic Compatibility (EMC) testing is a critical step in the development and certification of electronic devices to ensure they function correctly in their intended electromagnetic environment without causing or being susceptible to unacceptable electromagnetic interference [1]. EMC tests often involve applying specific electromagnetic test signals and monitoring the device's response or measuring its emissions. These test signals, particularly those used for immunity testing (e.g., transient pulses, modulated sine waves), are inherently time-series data with complex temporal characteristics. Accurately predicting the behavior or required parameters of these test signals under various conditions or extrapolating limited measurements could significantly optimize testing procedures, reduce test time, and improve the efficiency of EMC compliance efforts [27]. Traditional signal processing techniques [7, 11, 12, 13, 14] may struggle with the non-linear and potentially non-stationary nature of some EMC phenomena and test signals [5, 6]. Deep learning, specifically Long Short-Term Memory (LSTM) networks, has demonstrated exceptional capabilities in modeling and predicting complex sequential data [15, 16, 17, 18, 19]. This article proposes and outlines a methodology for predicting EMC test signal characteristics using LSTM networks. We discuss the conceptual framework for data acquisition, model architecture design, training, and evaluation, drawing upon principles from time-series analysis [5, 6], neural networks [2, 4, 10, 15, 16, 17, 18, 19], and signal processing [7, 11, 12, 13, 14]. The potential benefits include enhanced test efficiency, improved understanding of signal behavior, and the possibility of generating synthetic test data for simulation purposes [24].