The integration of a complex set Electronic Design Automation (EDA) tools to enhance interoperability is critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities natural processing and comprehension, offering novel approach interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent empowered by model, AutoMage, complemented serving as executors. ChatEDA streamlines the design flow...

The integration of a complex set Electronic Design Automation (EDA) tools to enhance interoperability is critical concern for circuit designers. Recent advancements in large language models (LLMs) have showcased their exceptional capabilities natural processing and comprehension, offering novel approach interfacing with EDA tools. This research paper introduces ChatEDA, an autonomous agent empowered by model, AutoMage, complemented serving as executors. ChatEDA streamlines the design flow...

Face-to-face (F2F) stacked 3D IC is a promising alternative for scaling beyond Moore’s Law. In F2F ICs, dies are connected through bonding terminals whose positions can significantly impact routing performance. Further, there exists resource competition among all the nets due to constrained terminal number. advanced technology nodes, traditional planning may also introduce legality challenges of terminals, as metal pitches be much smaller than sizes terminals. Previous works attempt insert...

Lake water level reflects the dynamic balance of input and output/loss is a sensitive indicator climate change variation. Studying relationship between closed Qinghai watershed important for understanding regional its impacts on lake. The objective this study was to investigate changes in level/area environmental factors during 1961–2019, using ground-based measurements data, hydrological model statistical methods. results revealed two primary phases: first phrase (1961–2004), lake lowered...

Learning feasible representation from raw gate-level netlists is essential for incorporating machine learning techniques in logic synthesis, physical design, or verification. Existing message-passing-based graph methodologies focus merely on topology while overlooking gate functionality, which often fails to capture underlying semantic, thus limiting their generalizability. To address the concern, we propose a novel netlist framework that utilizes contrastive scheme acquire generic...

Recently, with the development of tool-calling capabilities in large language models (LLMs), these have demonstrated significant potential for automating electronic design automation (EDA) flows by interacting EDA tool APIs via scripts. However, considering limited understanding tools, LLMs face challenges practical scenarios where diverse interfaces tools exist across different platforms. Additionally, flow often involves intricate, long-chain processes, increasing likelihood errors...

Large language models (LLMs) have shown significant promise in question-answering (QA) tasks, particularly retrieval-augmented generation (RAG) scenarios and long-context applications. However, their performance is hindered by noisy reference documents, which often distract from essential information. Despite fine-tuning efforts, Transformer-based architectures struggle to prioritize relevant content. This evidenced tendency allocate disproportionate attention irrelevant or later-positioned...

Logic synthesis, a critical stage in electronic design automation (EDA), optimizes gate-level circuits to minimize power consumption and area occupancy integrated (ICs). Traditional logic synthesis tools rely on human-designed heuristics, often yielding suboptimal results. Although differentiable architecture search (DAS) has shown promise generating from truth tables, it faces challenges such as high computational complexity, convergence local optima, extensive hyperparameter tuning....

The photolithography process is getting more sophisticated with technology node scaling down and VLSI designs becoming complex. As photomask patterns get finer, mask rule checks (MRCs) are inevitable to avoid discrepancies in the layout ensure manufacturability. This paper introduces an efficient checking approach that utilizes a representative edge sampling scheme. scheme selects subset of edges points each polygon capture its contour, meanwhile greatly reducing number involved actual...

As the scale of integrated circuits keeps increasing, it is witnessed that there a surge in research electronic design automation (EDA) to make technology node scaling happen. Graph great significance evolution since one most natural ways abstraction many fundamental objects EDA problems like netlist and layout, hence are essentially graph problems. Traditional approaches for solving these mostly based on analytical solutions or heuristic algorithms, which require substantial efforts...

Arithmetic block identification in gate-level netlist is an essential procedure for malicious logic detection, functional verification, or macro-block optimization. We argue that existing methods suffer either scalability performance issues. To address the problem, we propose a graph learning-based solution promises to extract desired components from complete design netlist. further novel asynchronous bidirectional neural network (ABGNN) dedicated representation learning on directed acyclic...

Automatic heuristic design through reinforcement learning opens a promising direction for solving computationally difficult problems. Unlike most previous works that aimed at solution construction, we explore the possibility of acquiring local search heuristics massive experiments. To illustrate applicability, an agent is trained to perform walk in space by selecting candidate neighbor each step. Specifically, target floorplanning problem, where generated perturbing sequence pair encoding...

Face-to-face (F2F) stacked 3D IC is a promising alternative for scaling beyond Moore's Law. In F2F ICs, dies are connected through bonding terminals whose positions can significantly impact routing performance. Further, there exists resource competition among all the nets due to constrained terminal number. advanced technology nodes, such integration may also introduce legality challenges of terminals, as metal pitches be much smaller than sizes terminals. Previous works attempt insert...

Super-resolution (SR) techniques aim to restore a high-resolution (HR) image from low-resolution (LR) images, which are often used assist the enhancement of image/video quality under rapid development HR and high-frame-rate media. Recently, neural network (NN)-based methods perform much better reconstruction than classical approaches. However, unacceptable computation complexity as well huge memory footprints NNs limit throughputs scalability these SR systems. In this work, we analyze...

The market benefits from a barrage of Ultra High Definition (Ultra-HD) displays, yet most extant cameras are barely equipped with Full-HD video capturing. In order to upgrade existing videos without extra storage costs, we propose an FPGA-based super-resolution system that enables real-time Ultra-HD upscaling in high quality. Our crops each frame into blocks, measures their total variation values, and dispatches them accordingly neural network or interpolation module for upscaling. This...

In this paper, we explore the burgeoning intersection of Large Language Models (LLMs) and Electronic Design Automation (EDA). We critically assess whether LLMs represent a transformative future for EDA or merely fleeting mirage. By analyzing current advancements, challenges, potential applications, dissect how can revolutionize processes like design, verification, optimization. Furthermore, contemplate ethical implications feasibility integrating these models into workflows. Ultimately,...

Deep learning frameworks optimize the computation graphs and intra-operator computations to boost inference performance on GPUs, while inter-operator parallelism is usually ignored. In this paper, a unified framework, AutoGraph, proposed obtain highly optimized in favor of parallel executions GPU kernels. A novel dynamic programming algorithm, combined with backtracking search, adopted explore optimal graph optimization solution, fast estimation from mixed critical path cost. Accurate...

Design rule checking (DRC) is essential in physical verification to ensure high yield and reliability for VLSI circuit designs. To achieve reasonable design cycle time, acceleration computationally intensive DRC tasks has been demanded accommodate the ever-growing complexity of modern circuits. In this paper, we propose X-Check, a GPU-accelerated checker. X-Check integrates novel parallel sweepline algorithms, which are both efficient practice with nontrivial theoretical guarantees....

Design rule checking (DRC) is an essential procedure in physical verification, yet few open-source DRC tools are accessible academia. To fill the gap, we present OpenDRC, engine that aims for extremely high efficiency. OpenDRC maintains hierarchical layouts with layer-wise bounding volume hierarchies and performs adaptive row-based partition to identify independent regions check pruning and/or parallel processing. For common design rules, provides a sequential mode runs cell-level...

Recent years have seen rising research in logic synthesis recipe generation to improve the Quality-of-Result (QoR). However, existing approaches typically low efficiency and are stuck at local optima. In this work, we propose a optimization framework, AlphaSyn, that incorporates domain-specific Monte Carlo tree search (MCTS) algorithm. AlphaSyn enables exploration across entire space while optimizing sampling points utilization. We further develop synthesis-specific upper confidence bound...