4 papers
AMDKernelVault: Large-Scale Datasets and Agentic Training for AMD GPU Kernel Optimization
Ji Liu, Saptarshi Majumder, Yiqing Huang +14
We introduce AMDKernelVault, an open HIP and Triton kernel corpus and training framework for recent AMD CDNA GPUs. Existing LLM-based kernel agents are largely CUDA/NVIDIA-centric…
AsmEvo: Agentic Assembly-Level Optimization of AMD GPU Kernels with Functional Equivalence Verification
Ji Liu, Puyuan Yang, Rongzhang Zheng +18
High-performance ML systems increasingly rely on GPU kernels whose editable source is unavailable, generated, or too distant from final machine code to expose remaining optimizatio…
PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding
Zihao An, Taichi Liu, Ziqiong Liu +3
Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verified in parallel by the target m…
PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
Zihao An, Huajun Bai, Ziqiong Liu +2
The autoregressive nature of large language models (LLMs) fundamentally limits inference speed, as each forward pass generates only a single token and is often bottlenecked by memo…