5 papers
AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents
Sharareh Younesian, Wenwen Ouyang, Sina Rafati +11
GPU kernel optimization is increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial low-level expertise. Recent AI…
Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling
Parsa Ashrafi Fashi, Utkarsh Saxena, Mehdi Rezagholizadeh +7
Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transformers, but most are still pretr…
DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking
Akash Haridas, Utkarsh Saxena, Parsa Ashrafi Fashi +3
Diffusion Transformers rely on static patchify tokenization, assigning the same token budget to smooth backgrounds, detailed object regions, noisy early timesteps, and late-stage r…
Zebra-Llama: Towards Extremely Efficient Hybrid Models
Mingyu Yang, Mehdi Rezagholizadeh, Guihong Li +2
With the growing demand for deploying large language models (LLMs) across diverse applications, improving their inference efficiency is crucial for sustainable and democratized acc…
X-EcoMLA: Upcycling Pre-Trained Attention into MLA for Efficient and Extreme KV Compression
Guihong Li, Mehdi Rezagholizadeh, Mingyu Yang +2
Multi-head latent attention (MLA) is designed to optimize KV cache memory through low-rank key-value joint compression. Rather than caching keys and values separately, MLA stores t…