125 citations · 361 across the 46 of their papers we have counts for
16 papers · 1 filter
QArray+: A physics-informed GPU-accelerated simulator for quantum dot arrays
Pranav Vaidhyanathan, Barnaby van Straaten, Alice Petrillo +6
Semiconductor quantum-dot arrays are a compelling platform for scalable quantum technologies, yet their practical operation is hindered by the complexity of tuning large-scale devi…
CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation
Hejia Zhang, Sheng Lu, Zhongming Yu +3
Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification i…
Component-Level Inverse Design of Transmon Qubits Using Neural Networks
Olivia Seidel, Firas Abouzahr, Abhishek Chakraborty +11
Designing a superconducting qubit to realize specific Hamiltonian parameters typically requires iterating through a time and compute-intensive forward loop in which the designer ch…
SCALE: Self-Supervised Constraint-Aware Layout GEneration for Local P&R DRV Fixing at Advanced Nodes
Chia-Tung Ho, Haoyu Yang, Guanglei Zhou +6
As semiconductor manufacturing advances toward sub-2nm nodes, local place-and-route (P&R) design-rule violation (DRV) fixing is increasingly limited by complex rule interactions, d…
EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair
Bing-Yue Wu, Chia-Tung Ho, Haoyu Yang +2
Design rule check (DRC) closure remains a major bottleneck in advanced-node physical design. Although detailed routers are rule-aware, residual design rule violations (DRVs) often…
Agentic Hardware Design as Repository-Level Code Evolution
Cunxi Yu, Chenhui Deng, Nathaniel Pinckney +1
We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution. A Markdown harness is compiled into a project pack containing do…
AUTOGATE: Automated Clock Gating via Toggling-Aware LLM-based RTL Rewriting
Yiting Wang, Chenhui Deng, Chia-Tung Ho +6
Fine-grain clock gating (FGCG) is among the most effective techniques for reducing dynamic power, yet current FGCG optimization flows remain largely manual. Recent LLM-based RTL op…
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +572
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction
Zhenbang Du, Kejing Xia, Xinrui Zhong +6
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding…
How LLMs Fail and Generalize in RTL Coding for Hardware Design?
Guan-Ting Liu, Chao-Han Huck Yang, Chenhui Deng +3
Translating sequential programming priors into the parallel temporal logic of hardware design remains a crucial bottleneck for large language models(LLM). To investigate this, we i…
Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling
Taylor Lee Patti, Paavai Pari, Yang Gao +5
Quantum trajectory methods reduce the computational overhead of simulating noisy quantum systems, approximating them with stochastically sampled -entry quantum statevector…
GaloisSAT: Differentiable Boolean Satisfiability Solving via Finite Field Algebra
Curie Kim, Carsten Portner, Mingju Liu +6
Boolean satisfiability (SAT) problem, the first problem proven to be NP-complete, has become a fundamental challenge in computational complexity, with widespread applications in op…
ACE-RTL: When Agentic Context Evolution Meets RTL-Specialized LLMs
Chenhui Deng, Zhongzhi Yu, Guan-Ting Liu +3
Recent advances in LLMs have sparked growing interest in applying them to hardware design automation, particularly for accurate RTL code generation. Prior efforts follow two largel…
QuRL: Efficient Reinforcement Learning with Quantized Rollout
Yuhang Li, Reena Elangovan, Xin Dong +2
Reinforcement learning with verifiable rewards (RLVR) has become a trending paradigm for training reasoning large language models (LLMs). However, due to the autoregressive decodin…
LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
Hejia Zhang, Zhongming Yu, Chia-Tung Ho +3
Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making online reinforcement learning (…
GRPO with State Mutations: Improving LLM-Based Hardware Test Plan Generation
Dimple Vijay Kochar, Nathaniel Pinckney, Guan-Ting Liu +4
RTL design often relies heavily on ad-hoc testbench creation early in the design cycle. While large language models (LLMs) show promise for RTL code generation, their ability to re…