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Yao Lai

7 accepted papers

2026

Fast Inverse Lithography via GRPO Reinforced Flow Matching

ICML 2026poster

In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask. As circuit features shrink below the wavelength of light, optical diffraction causes the printed patterns to deviate from their intended layouts. Inverse Lithography Technology (ILT) add…

Cited by 0SourceScholar
2026

KernelCraft: Benchmarking for Agentic Close-to-Metal Kernel Generation on Emerging Hardware

ICML 2026poster

New AI accelerators with novel instruction set architectures (ISAs) often require developers to manually craft low-level kernels - a time-consuming, laborious, and error-prone process that cannot scale across diverse hardware targets. This prevents emerging hardware platforms from reaching the marke…

Cited by 0SourceScholar
2025

AnalogCoder: Analog Circuit Design via Training-Free Code Generation

AAAI 2025technical

Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functionality. Despite advances made by Large Language Models (LLMs) in digital circuit design, the complexity and scarcity of da…

2025

FUDOKI: Discrete Flow-based Unified Understanding and Generation via Kinetic-Optimal Velocities

NeurIPS 2025spotlight

The rapid progress of large language models (LLMs) has catalyzed the emergence of multimodal large language models (MLLMs) that unify visual understanding and image generation within a single framework. However, most existing MLLMs rely on autoregressive (AR) architectures, which impose inherent lim…

Cited by 0SourceScholar
2024

Scalable and Effective Arithmetic Tree Generation for Adder and Multiplier Designs

NeurIPS 2024spotlight

Across a wide range of hardware scenarios, the computational efficiency and physical size of the arithmetic units significantly influence the speed and footprint of the overall hardware system. Nevertheless, the effectiveness of prior arithmetic design techniques proves inadequate, as they do not su…

2023

ChiPFormer: Transferable Chip Placement via Offline Decision Transformer

ICML 2023poster

Placement is a critical step in modern chip design, aiming to determine the positions of circuit modules on the chip canvas. Recent works have shown that reinforcement learning (RL) can improve human performance in chip placement. However, such an RL-based approach suffers from long training time an…

Cited by 49SourcePDFScholar
2022

MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

NeurIPS 2022accept

Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement l…