3 citations · 7 across the 12 of their papers we have counts for
6 papers · 1 filter
Inference-Native Zeroth-Order Optimization
Zelin Li, Caiwen Ding
Zeroth-order (ZO) optimization removes backpropagation, but conventional implementations still create candidate states by mutating model weights and materialize updates through the…
GROOT: Graph Edge Re-growth and Partitioning for the Verification of Large Designs in Logic Synthesis
Kiran Thorat, Hongwu Peng, Yuebo Luo +8
Traditional verification methods in chip design are highly time-consuming and computationally demanding, especially for large scale circuits. Graph neural networks (GNNs) have gain…
CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
Zijian Zhang, Rong Wang, Shiyang Li +3
Developing efficient CUDA kernels is increasingly critical for AI applications such as large-scale LLM training. However, manual kernel design is both costly and time-consuming, mo…
Zero-Space Cost Fault Tolerance for Transformer-based Language Models on ReRAM
Bingbing Li, Geng Yuan, Zigeng Wang +6
Resistive Random Access Memory (ReRAM) has emerged as a promising platform for deep neural networks (DNNs) due to its support for parallel in-situ matrix-vector multiplication. How…
Advanced Large Language Model (LLM)-Driven Verilog Development: Enhancing Power, Performance, and Area Optimization in Code Synthesis
Kiran Thorat, Jiahui Zhao, Yaotian Liu +5
The increasing use of Advanced Language Models (ALMs) in diverse sectors, particularly due to their impressive capability to generate top-tier content following linguistic instruct…
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
Hongwu Peng, Xi Xie, Kaustubh Shivdikar +6
In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory acc…