A primary motivation for analog integrated circuit (IC) design automation is the inefficiency of manual design in meeting increasingly stringent specifications, which often involve over 10 objectives. Recent advances in reinforcement learning (RL) emerge as a promising method, yet gaps remain when considering full design specifications, especially under process-voltage-temperature (PVT) variations. Excessive objectives lead to diminished reward signals, while varying PVT conditions result in conflicting gradients, both of which result in inefficient exploration. To address these, we propose a priority-based graph-enhanced RL framework. Specifically, using fuzzy logic converts quantitative rewards into qualitative priority signals, mitigating reward deterioration and enhancing exploration via entropy regularization. Furthermore, a graph-based representation compresses high-dimensional objective spaces under PVT variations into low-dimensional manifolds, enabling dynamic resource allocation to variation-sensitive regions and resolving gradient conflicts. Empirical results on various real-world analog ICs demonstrate that our method significantly outperforms existing RL algorithms, achieving superior solution quality and reducing simulation overhead.

Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts.The code is available at https://jianxliao.github.io/cadllm-page/

Retrieval-Augmented Generation (RAG) provides large language models (LLMs) a means of retrieving relevant external knowledge, but its document parsing leads to increased latency and energy consumption. To address this issue, we propose a dynamically reconfigurable Neural Processing Unit (NPU) that accelerates both RAG document parsing and inference. By leveraging compute-in-memory fusion, dynamic convolution, and multi-level parallelism, our approach reduces memory transfer overhead and optimizes hardware resource allocation. Experimental results show that our design achieves a 1.8x speedup in document parsing and a 2.83x improvement in energy efficiency. Additionally, it achieves an 11.71% reduction in inference time and a 35.59% boost in energy efficiency over traditional CPU/GPU methods, offering a scalable solution for large-scale RAG tasks.

At present, accurate detection of small objects in an aerial imagery remains a challenge in remote sensing due to limited pixel resolution, background clutter, and scale variations. To address these issues for high-precision detection in a complex remote sensing scene, we propose a novel detection framework based on RepViT Dynamic Fusion and YOLOv11, termed RDF-YOLO. The RDF-YOLO brings in two core innovations:(1) a Dynamic Scale RepViT module that integrates lightweight Transformer operations into the backbone to enhance global context modeling and semantic discrimination under noisy conditions, and (2) a dynamic fusion module that incorporates spatially aware dilated convolutions and channel-adaptive fusion strategies to enable flexible, scale-aware feature interaction. Extensive experiments on the challenging AI-TOD dataset show that the RDF-YOLO outperforms state-of-the-art methods by substantial margins. In particular, the RDF-YOLO improves AP50:95 by 6.9% and AP50by 8.6% over the YOLOv11 baseline and on small-object metrics, including APvt, APt, and APs. These results verify the effectiveness of the RDF-YOLO architecture for robust and efficient detection of small objects in remote sensing imagery. The source code is available at https://github.com/AssiiKk/RDF-YOLO.

With the proliferation of artificial intelligence, Computer-Aided Design (CAD) is being transformed into Computer-Automated Design (CAutoD). In this paper, the advent of Large Language Models (LLMs) introduces new opportunities for CAutoD. This study develops AutoForma, an LLM-based multi-agent system, for automatic conversion from natural language descriptions to 3D models. By harnessing the comprehension capabilities of LLMs, AutoForma streamlines the CAutoD workflow by efficiently translating design intents into precise models in CAD. Through a comprehensive set of evaluations, AutoForma is seen to offer automation performance across various design tasks, particularly in generating non-standard parts that meet specific requirements, with higher efficiency and accuracy than using just an LLM like GPT-4.