A chiplet strategy that reduces per-die fabrication cost can still lose much of that advantage at the validation stage, and this article argues that most chiplet economics literature simply does not account for that risk. Emulation and validation capacity for multi-die programs is typically forecast by extrapolating from a prior monolithic-SoC generation’s emulator footprint, an approach that appears to understate the capacity growth driven by cross-die interface count and protocol complexity. The framework proposed here treats validation capacity as a first-class capital and organizational commitment established at program inception, then revisited at three defined program milestones – an early architecture-gate sizing estimate, a subsystem-gate reforecast once per-die RTL stabilizes, and an integration-gate provisioning commitment ahead of multi-die bring-up. The argument draws on capacity-planning and capital-governance experience across four flagship SoC generations and a large emulation asset base exceeding a billion dollars in value, including program-level outcomes from capacity-doubling initiatives, system virtualization, and structured vendor negotiation. These outcomes suggest that staged, milestone-linked capacity governance can materially improve capital efficiency relative to linear extrapolation, though the comparison offered is qualitative rather than a controlled trial. Chiplet economics, as currently framed in the literature, appears incomplete without an explicit validation-capacity line item; without it, capacity shortfalls tend to surface only once multi-die bring-up is already underway – precisely the point where a program has the least room to absorb schedule risk. The three-gate model is proposed as a transferable planning discipline, independent of process node or specific die-partitioning choice.
El presente trabajo analiza los algoritmos de Machine Learning empleados para la detección del burnout en profesionales tecnológicos, problemática que constituye uno de los principales retos para el sector de tecnología en la actualidad. El estudio identifica las técnicas de aprendizaje automático que han adquirido mayor relevancia debido a su capacidad de generar resultados confiables y eficaces en contextos donde la incorporación de tecnologías avanzadas resulta cada vez más importante, así como identifica patrones de burnout que se vienes manifestando en el sector La investigación adopta una metodología basada en los lineamientos establecidos por la declaración PRISMA y considera fuentes bibliográficas especializadas como Scopus, IEEE Xplore, Science Direct y Web Of Science. Después de aplicar los criterios de selección definidos, se incluyeron 18 estudios para el análisis. Los resultados evidenciaron que las investigaciones seleccionadas realizan diversas comparaciones entre algoritmos, destacando principalmente máquinas de vectores de soporte (SVM), algoritmos basados en árboles, Random Forest, métodos de boosting/XGBoost, modelos difusos y técnicas de aprendizaje profundo, así como los factores que muestran mayor consistencia entre los estudios incluyen la sobrecarga laboral, las exigencias temporales, la fatiga, el desequilibrio entre esfuerzo y recompensa, la insuficiencia de apoyo o reconocimiento y las dificultades para conciliar las responsabilidades laborales y personales
Designing AI-enabled video surveillance systems has become far more challenging than simply deciding the number of cameras or estimating storage. Once AI inference is performed on cameras or edge devices, every design decision influences several others. For example, increasing the pixel density needed for facial recognition affects lens selection, bitrate, storage capacity, and network bandwidth, while adding more AI analytics changes edge-computing requirements and software licensing. As a result, conventional CCTV planning methods are no longer sufficient for modern AI deployments. This paper presents the architecture of IndoAI’s integrated planning toolkit, which follows a three-stage workflow ie Design, Size, and Verify, using nine specialised calculators for camera placement, infrastructure sizing, and deployment validation. Alongside these tools, an Appization-based AI Agent License Sizing and ROI Predictor estimates software licensing requirements and deployment economics. Building on recent advances in Vision Language Models (VLMs) and platform economics, we propose a VLM as the natural-language interface to this planning framework. Users can describe surveillance requirements using text, photographs or floor plans instead of manually entering technical parameters. The VLM converts these inputs into structured data for the calculator suite. We also present two systems under development: a VLM-driven video intelligence pipeline that analyses surveillance footage to generate timestamped event detections, evaluated using the UCF-Crime dataset and a VLM-assisted Bill of Quantities generator that produces annotated camera layouts together with storage, bandwidth and licensing estimates for deployment planning.
Large-scale Large Language Model (LLM) inference systems deployed in distributed cloud environments face significant challenges in maintaining low latency, efficient GPU utilization, and energy-aware scheduling across heterogeneous hardware. Traditional Kubernetes-based orchestration frameworks are not optimized for the dynamic memory and compute characteristics of transformer workloads, often resulting in resource fragmentation and increased scheduling latency. This paper proposes Adaptive Resource Orchestration (ARO), a telemetry-driven framework designed for distributed LLM inference in heterogeneous GPU clusters. ARO introduces a Rack Affinity Group (RAG) hierarchical indexing mechanism that reduces scheduling search complexity to O(log N * M) while enabling topology-aware resource placement. The framework integrates multi-objective optimization to balance inference latency, energy efficiency, and GPU utilization. Experimental evaluation on a 32-node heterogeneous GPU cluster (H100, A100, and T4) demonstrates significant improvements over baseline Kubernetes scheduling approaches, including up to 84% reduction in P99 latency and 112% improvement in inference energy efficiency (68 tokens/J). These results highlight the importance of hardware-aware orchestration for improving performance and energy efficiency in distributed AI infrastructure.
This study examines technological strategies implemented to prevent examination malpractices in higher education, focusing on secure platforms, biometric authentication, online proctoring, and adaptive assessment designs. Employing a hybrid research framework, it integrates quantitative surveys, qualitative interviews, pilot program observations, and platform analytics to evaluate efficacy and user experience across diverse academic disciplines. Findings indicate that multi-layered security configurations combining biometric verification, AI-assisted proctoring, randomized question delivery, and blockchain-based audit trails reduce misconduct incidence while maintaining fairness and user acceptance. The research highlights the importance of balancing automated detection with human oversight, ensuring infrastructural readiness, and providing transparent communication to address privacy and usability concerns. Results also reveal discipline-specific variations in adoption and effectiveness, with STEM fields showing greater gains linked to interactive assessment formats. The integration of generative AI tools presents both opportunities for enhanced feedback and challenges related to potential exploitation, underscoring the need for careful design and monitoring. Hence, the outcomes suggest that thoughtfully implemented technological interventions can support academic integrity by aligning security measures with pedagogical objectives and stakeholder engagement
