In the era of big data, ensuring the quality of datasets has become increasingly crucial across various domains. We propose a comprehensive framework designed to automatically assess and rectify data quality issues in any given dataset, regardless of its specific content, focusing on both textual and numerical data. Our primary objective is to address three fundamental types of defects: absence, redundancy, and incoherence. At the heart of our approach lies a rigorous demand for both explainability and interpretability, ensuring that the rationale behind the identification and correction of data anomalies is transparent and understandable. To achieve this, we adopt a hybrid approach that integrates statistical methods with machine learning algorithms. Indeed, by leveraging statistical techniques alongside machine learning, we strike a balance between accuracy and explainability, enabling users to trust and comprehend the assessment process. Acknowledging the challenges associated with automating the data quality assessment process, particularly in terms of time efficiency and accuracy, we adopt a pragmatic strategy, employing resource-intensive algorithms only when necessary, while favoring simpler, more efficient solutions whenever possible. Through a practical analysis conducted on a publicly provided dataset, we illustrate the challenges that arise when trying to enhance data quality while keeping explainability. We demonstrate the effectiveness of our approach in detecting and rectifying missing values, duplicates and typographical errors as well as the challenges remaining to be addressed to achieve similar accuracy on statistical outliers and logic errors under the constraints set in our work.

Open-source EDA tools are rapidly advancing, fostering collaboration, innovation, and knowledge sharing within the EDA community. However, the growing complexity of these tools, characterized by numerous design parameters and heuristics, poses a significant barrier to their widespread adoption. This complexity is particularly pronounced in integrated circuit (IC) backend designs, which place substantial demands on engineers' expertise in EDA tools. To tackle this challenge, we introduce IICPilot, an intelligent IC backend design system based on LLM technology. IICPilot automates various backend design procedures, including script generation, EDA tool invocation, design space exploration of EDA parameters, container-based computing resource allocation, and exception management. By automating these tasks, IICPilot significantly lowers the barrier to entry for open-source EDA tools. Specifically, IICPilot utilizes LangChain's multi-agent framework to efficiently handle distinct design tasks, enabling flexible enhancements independently. Moreover, IICPilot separates the backend design workflow from specific open-source EDA tools through a unified EDA calling interface. This approach allows seamless integration with different open-source EDA tools like OpenROAD and iEDA, streamlining the backend design and optimization across the EDA tools.

Cloud computing accelerates design space exploration in logic synthesis, and parameter tuning in physical design. However, deploying EDA jobs on the cloud requires EDA teams to deeply understand the characteristics of their jobs in cloud environments. Unfortunately, there has been little to no public information on these characteristics. Thus, in this paper, we formulate the problem of migrating EDA jobs to the cloud. First, we characterize the performance of four main EDA applications, namely: synthesis, placement, routing and static timing analysis. We show that different EDA jobs require different machine configurations. Second, using observations from our characterization, we propose a novel model based on Graph Convolutional Networks to predict the total runtime of a given application on different machine configurations. Our model achieves a prediction accuracy of 87%. Third, we develop a new formulation for optimizing cloud deployments in order to reduce deployment costs while meeting deadline constraints. We present a pseudo-polynomial optimal solution using a multi-choice knapsack mapping that reduces costs by 35.29%.

Engineering companies working with machine tools will often be required to calibrate those machines to international standards. The calibration process requires various errors in the machine to be measured by a skilled expert. In addition to conducting the tests, the engineer must also plan the order in which the tests should take place, and also which instruments should be used to perform each test. It is critical to find as short a calibration plan as possible so that the machine is not out of service for too long. In this work, automated planning is applied to the problem of generating machine tool calibration plans. Given a description of a machine, and its various axes, we produce a calibration plan that minimises the time taken to measure all of the errors of a machine. We also consider the case when there is not enough time to test all errors of the machine, and the calibration plan must maximise the importance of the set of errors tested in the limited time available.