125 citations · 361 across the 46 of their papers we have counts for
11 papers · 1 filter
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…
Marco: Configurable Graph-Based Task Solving and Multi-AI Agents Framework for Hardware Design
Chia-Tung Ho, Jing Gong, Yunsheng Bai +3
Hardware design presents numerous challenges stemming from its complexity and advancing technologies. These challenges result in longer turn-around-time (TAT) for optimizing perfor…
LO-BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference
Reena Elangovan, Charbel Sakr, Anand Raghunathan +1
Post-training quantization (PTQ) is a promising approach to reducing the storage and computational requirements of large language models (LLMs) without additional training cost. Re…
SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity
Zichen Fan, Steve Dai, Rangharajan Venkatesan +2
Diffusion models have gained significant popularity in image generation tasks. However, generating high-quality content remains notably slow because it requires running model infer…