11 citations · 11 across the 9 of their papers we have counts for
10 papers
EARL: Entropy-Aware RL Alignment of LLMs for Reliable RTL Code Generation
Jiahe Shi, Zhengqi Gao, Ching-Yun Ko +1
Recent advances in large language models (LLMs) have demonstrated significant potential in hardware design automation, particularly in using natural language to synthesize Register…
SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Training
Pingchuan Ma, Ziang Yin, Qi Jing +8
DONNs leverage light propagation for efficient analog AI and signal processing. Advances in nanophotonic fabrication and metasurface-based wavefront engineering have opened new pat…
RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning
Kaiwen Zha, Zhengqi Gao, Maohao Shen +3
Reinforcement learning (RL) has recently emerged as a compelling approach for enhancing the reasoning capabilities of large language models (LLMs), where an LLM generator serves as…
MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure
Pingchuan Ma, Zhengqi Gao, Meng Zhang +5
Inverse design has emerged as a transformative approach for photonic device optimization, enabling the exploration of high-dimensional, non-intuitive design spaces to create ultra-…
REG: Rectified Gradient Guidance for Conditional Diffusion Models
Zhengqi Gao, Kaiwen Zha, Tianyuan Zhang +2
Guidance techniques are simple yet effective for improving conditional generation in diffusion models. Albeit their empirical success, the practical implementation of guidance dive…
BOSON: Understanding and Enabling Physically-Robust Photonic Inverse Design with Adaptive Variation-Aware Subspace Optimization
Pingchuan Ma, Zhengqi Gao, Amir Begovic +6
Nanophotonic device design aims to optimize photonic structures to meet specific requirements across various applications. Inverse design has unlocked non-intuitive, high-dimension…
PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional Neural Operator for Ultra-Fast Photonic Device FDTD Simulation
Pingchuan Ma, Haoyu Yang, Zhengqi Gao +2
The finite-difference time-domain (FDTD) method, which is important in photonic hardware design flow, is widely adopted to solve time-domain Maxwell equations. However, FDTD is kno…
Rare Event Probability Learning by Normalizing Flows
Zhenggqi Gao, Dinghuai Zhang, Luca Daniel +1
A rare event is defined by a low probability of occurrence. Accurate estimation of such small probabilities is of utmost importance across diverse domains. Conventional Monte Carlo…
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
Chih-Yu Lai, Fan-Keng Sun, Zhengqi Gao +2
Time series anomaly detection is challenging due to the complexity and variety of patterns that can occur. One major difficulty arises from modeling time-dependent relationships to…
KirchhoffNet: A Scalable Ultra Fast Analog Neural Network
Zhengqi Gao, Fan-Keng Sun, Ron Rohrer +1
In this paper, we leverage a foundational principle of analog electronic circuitry, Kirchhoff's current and voltage laws, to introduce a distinctive class of neural network models…