Despite the transformative potential of Large Language Models (LLMs) in hardware design, a comprehensive evaluation of their capabilities in design verification remains underexplored. Current efforts predominantly focus on RTL generation and basic debugging, overlooking the critical domain of functional verification, which is the primary bottleneck in modern design methodologies due to the rapid escalation of hardware complexity. We present FIXME, the first end-to-end, multi-model, and open-source evaluation framework for assessing LLM performance in hardware functional verification (FV) to address this crucial gap. FIXME introduces a structured three-level difficulty hierarchy spanning six verification sub-domains and 180 diverse tasks, enabling in-depth analysis across the design lifecycle. Leveraging a collaborative AI-human approach, we construct a high-quality dataset using 100% silicon-proven designs, ensuring comprehensive coverage of real-world challenges. Furthermore, we enhance the functional coverage by 45.57% through expert-guided optimization. By rigorously evaluating state-of-the-art LLMs such as GPT-4, Claude3, and LlaMA3, we identify key areas for improvement and outline promising research directions to unlock the full potential of LLM-driven automation in hardware design verification. The benchmark is available at https://github.com/ChatDesignVerification/FIXME.

With the ever-increasing data volume and application diversity, a modern data analytics job is generally built as a workflow consisting of multiple tasks. For either specific functionalities or higher performance, tasks in a workflow may need to be deployed on different data processing platforms. This article proposes CLIC, a highly extensible system for efficient cross-platform data analytics. To leverage the advantage of diverse platforms while alleviating development efforts, we propose an embedding-based operator encoding scheme and a Graph Convolutional Network model for efficient platform selection. Aiming at flexibly integrating new operators and platforms, CLIC is designed with a highly extensible system architecture that decouples the core functionalities from backend platforms. Experiments show that CLIC can significantly improve the performance of modern data analysis workflows with fast platform selection.