NPS: A Framework for Accurate Program Sampling Using Graph Neural Network

With the end of Moore's Law, there is a growing demand for rapid architectural innovations in modern processors, such as RISC-V custom extensions, to continue performance scaling. Program sampling is a crucial step in microprocessor design, as it selects representative simulation points for workload simulation. While SimPoint has been the de-facto approach for decades, its limited expressiveness with Basic Block Vector (BBV) requires time-consuming human tuning, often taking months, which impedes fast innovation and agile hardware development. This paper introduces Neural Program Sampling (NPS), a novel framework that learns execution embeddings using dynamic snapshots of a Graph Neural Network. NPS deploys AssemblyNet for embedding generation, leveraging an application's code structures and runtime states. AssemblyNet serves as NPS's graph model and neural architecture, capturing a program's behavior in aspects such as data computation, code path, and data flow. AssemblyNet is trained with a data prefetch task that predicts consecutive memory addresses. In the experiments, NPS outperforms SimPoint by up to 63%, reducing the average error by 38%. Additionally, NPS demonstrates strong robustness with increased accuracy, reducing the expensive accuracy tuning overhead. Furthermore, NPS shows higher accuracy and generality than the state-of-the-art GNN approach in code behavior learning, enabling the generation of high-quality execution embeddings

[In Press] Delirium care knowledge of critical care nurses : a multiple-choice question based quiz

Background: Lack of evidence regarding whether a useful examination instrument such as an multiple choice question (MCQ) quiz is reliable for assessing delirium care knowledge. Aims and objectives: To develop and psychometrically test a MCQ-based quiz for assessing the delirium care knowledge of critical care nurses. Design: Instrument development and psychometric evaluation study. Methods: The development and validation process consisted of two phases. The first Phase focused on the quiz development, which was achieved through the following steps: (a) generation of an initial 20-item pool; (b) assessment of content validity; (c) assessment of face validity; (d) conduction of a pilot test, involving the collection of data from 217 critical care nurses through an online survey; and (e) item analysis and item elimination according to item difficulty and discrimination indices. The MCQ quiz was finalized through the development process. The second phase emphasized quiz validation through estimation of the internal consistency, split-half and test–retest reliability, and construct validity using parallel analysis with exploratory factor analysis (EFA). Results: A final 16-item MCQ quiz was emerged from the item analysis. The Kuder– Richardson formula 20 coefficient for the overall quiz indicated good internal consistency (0.85), and the intraclass correlation coefficient with a 30-day interval also indicated that the questionnaire had satisfactory stability (0.97). EFA confirmed that the quiz had appropriate construct validity, and four factors could explain 60.87% of the total variance. Conclusion: In this study, the MCQ, and single best answer quiz for assessing delirium care knowledge was developed, and its reliability and validity for this purpose were demonstrated. Relevance to clinical practice: This study introduced an evidence-based quiz designed for future use in delirium care research and education that has significant implications for MCQ-based knowledge assessment in clinical practice