arXiv subjects

Yuntao Lu

Publications and source records attributed to Yuntao Lu.

5 recordsLinked to original sources

arXiv2026

MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms

Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impact on the final quality of result (QoR), and macro legalization is typically the final step in determining the macro positions. However, existing approaches related to macro legalization either lack robustness or incur substantial computational costs or neglect the regularity between macros. To address these limitations, we introduce MacroAgent. The novel framework is a four-stage approach: clustering, contour generation, template matching, and inter-cluster refinement. We propose leveraging Large Language Models (LLMs) to discover multiple, effective heuristic regularity-aware contour algorithms. This framework successfully generates robust and effective algorithmic solutions for macro legalization. Compared with state-of-the-art macro legalization works, experimental results on TILOS and Chipyard benchmarks demonstrate a 2 to 8 fold improvement in layout regularity, a 3% to 5% reduction in routed wirelength with comparable congestion after global routing, and significantly better robustness with an acceptable runtime. Furthermore, end-to-end evaluation through Cadence Innovus place-and-route confirms that the regularity improvements translate into tangible PPA gains, including 2.9% lower routed wirelength and 68.3% TNS improvement over the DREAMPlace macro legalization baseline; it also achieves 1.8% lower routed wirelength when integrated into the Innovus macro placement flow.

cs.LG↗

arXiv2026

Agentic Electronic Design Automation: A Handoff Perspective

Electronic design automation (EDA) is multi-stage and handoff-heavy, relying on transfers among humans, design artifacts, and multiple tools. LLM-based agents now participate in these transfers, yet the resulting research landscape is highly fragmented and lacks a unified perspective. This survey adopts the primary input--output pair as its organizing lens: for each agent system, we analyze its primary, supporting, and intermediate handoff objects and classify it according to the provenance of its primary input and the consumer boundary of its primary output. Intra-stage systems operate on objects within a single EDA stage, inter-stage systems transform EDA-native artifacts into forms usable by downstream stages, and extra-stage systems translate human intent into EDA artifacts. Based on this taxonomy, we survey 115 representative systems and examine them along multiple dimensions, including research trends, benchmarks, and model mechanisms. Finally, we outline an agentic EDA protocol roadmap and discuss open problems for future research.

cs.SE↗

arXiv2025

Asymptotics of Protein Number Distribution in Stochastic Gene Expression Models under Burst Approximation

The burst approximation is a widely used technique to simplify stochastic gene expression models. However, the dynamics and analytical properties of the protein number distribution in gene expression models under the burst approximation are barely studied. In this study, we propose and systematically analyze surrogate models with multiple gene states and arbitrary burst size distributions. An analytical time-dependent solution to the chemical master equation is derived and then exploited in two directions. Theoretically, several fine properties of the protein number distribution are established using functional analysis. For geometrically distributed burst sizes, the distribution is dominated by a scaled negative binomial distribution, and is light-tailed in certain parameter regimes. Computationally, we develop efficient algorithms in three settings, enabling fast calculation of the protein number distribution. Furthermore, the approximation error relative to full gene expression models is estimated in terms of low-order moments of the distribution, thereby clarifying the validity of the burst approximation.

physics.bio-ph↗

arXiv2025

Stochastic Kinetics of mRNA Molecules in a General Transcription Model

Stochastic modeling of transcription is a classic yet long-standing problem in theoretical biophysics. The lack of unified results and a computationally efficient approach for a general, fine-grained transcription model has confined relevant research to some over-simplified special cases like the Telegraph model. This article establishes a general, unified and computationally efficient framework for studying stochastic transcription kinetics. We consider a chemical reaction model of transcription and construct the time-dependent solution to the corresponding chemical master equation. A well-known matrix-form expression for steady-state binomial moments is recovered by calculating the temporal limit of the time-dependent dynamics. Two novel inequalities for binomial moments and the probability mass function are derived using techniques from functional analysis. It follows that the distribution of mRNA counts is upper-bounded by a constant multiple of Poisson distribution, thus mathematically proving the main statement of the Heavy-Tailed Law. Additionally, the standard binomial moment method is analyzed from a numerical perspective, where truncation error is estimated using our inequalities. Compared with some widely-used numerical methods, a key advantage of this result is the significantly lower computational complexity.

physics.bio-ph↗

arXiv2025

Approximation Error of the Burst Approximation for a Stochastic Gene Expression Model

Stochastic modeling of gene expression is a classic problem in theoretical biophysics, and the burst approximation is widely used to simplify gene expression models formulated via the chemical master equation. However, the approximation error has been investigated only for the simplest case. This article proposes and analyzes a general stochastic gene expression model with an arbitrary number of gene states, and quantifies the error introduced by the burst approximation. Using the standard binomial moment method, we derive recurrence relations for binomial moments in steady state. We develop an algorithm to numerically compute binomial moments in a hierarchical manner. In particular, explicit expressions for low-order moments are presented. Compared with surrogate models under the burst approximation, we conclude that the first-order moment of protein counts is preserved, whereas discrepancies generally arise in higher-order moments. By estimating the difference between two second-order moments using functional analysis, we evaluate the validity of the burst approximation.

physics.bio-ph↗