125 citations · 361 across the 46 of their papers we have counts for
48 papers
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…
GalaxyDiT: Efficient Video Generation with Guidance Alignment and Adaptive Proxy in Diffusion Transformers
Zhiye Song, Steve Dai, Ben Keller +1
Diffusion models have revolutionized video generation, becoming essential tools in creative content generation and physical simulation. Transformer-based architectures (DiTs) and c…
TurboSAT: Gradient-Guided Boolean Satisfiability Accelerated on GPU-CPU Hybrid System
Steve Dai, Cunxi Yu, Kalyan Krishnamani +1
While accelerated computing has transformed many domains of computing, its impact on logical reasoning, specifically Boolean satisfiability (SAT), remains limited. State-of-the-art…
ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models
Akshat Ramachandran, Marina Neseem, Charbel Sakr +3
The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (KV) cache, quickly overwhelming…
Pretraining Large Language Models with NVFP4
NVIDIA, Felix Abecassis, Anjulie Agrusa +87
Large Language Models (LLMs) today are powerful problem solvers across many domains, and they continue to get stronger as they scale in model size, training set size, and training…
Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification
Nathaniel Pinckney, Chenhui Deng, Chia-Tung Ho +5
We present the Comprehensive Verilog Design Problems (CVDP) benchmark, a new dataset and infrastructure to advance LLM and agent research in hardware design and verification. CVDP…
Augmenting Simulated Noisy Quantum Data Collection by Orders of Magnitude Using Pre-Trajectory Sampling with Batched Execution
Taylor L. Patti, Thien Nguyen, Justin G. Lietz +2
Classically simulating quantum systems is challenging, as even noiseless -qubit quantum states scale as . The complexity of noisy quantum systems is even greater, requiring…
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
Coleman Hooper, Charbel Sakr, Ben Keller +4
Quantization is a powerful tool to improve large language model (LLM) inference efficiency by utilizing more energy-efficient low-precision datapaths and reducing memory footprint.…
GauRast: Enhancing GPU Triangle Rasterizers to Accelerate 3D Gaussian Splatting
Sixu Li, Ben Keller, Yingyan Celine Lin +1
3D intelligence leverages rich 3D features and stands as a promising frontier in AI, with 3D rendering fundamental to many downstream applications. 3D Gaussian Splatting (3DGS), an…