1 citations · 1 across the 8 of their papers we have counts for
9 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…
RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
Chih-Yu Lai, Yu-Chien Ning, Duane S. Boning
Probabilistic Time Series Forecasting (PTSF) plays a critical role in domains requiring accurate and uncertainty-aware predictions for decision-making. However, existing methods of…
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…
Simple Feedfoward Neural Networks are Almost All You Need for Time Series Forecasting
Fan-Keng Sun, Yu-Cheng Wu, Duane S. Boning
Time series data are everywhere -- from finance to healthcare -- and each domain brings its own unique complexities and structures. While advanced models like Transformers and grap…
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…