cuDNN: Efficient Primitives for Deep Learning
PulseAugur coverage of cuDNN: Efficient Primitives for Deep Learning — every cluster mentioning cuDNN: Efficient Primitives for Deep Learning across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
-
Chinese Parsers DeepDoc, MinerU Crossover in Japanese RAG Performance
A comparative analysis of two Chinese open-source document parsers, DeepDoc and MinerU, for Japanese RAG systems reveals a crossover performance based on the retrieval method used. DeepDoc demonstrated superior results …
-
New method uses world models to speed up tensor program optimization
Researchers have developed a novel approach to optimize tensor programs for machine learning systems by modeling schedule evaluation as latent dynamics. This method, inspired by world models, uses a lightweight transiti…
-
Together AI releases FlashAttention-3 and -4 for faster LLM processing
Together AI has released FlashAttention-3 and FlashAttention-4, significant upgrades to their GPU-accelerated attention mechanism for large language models. FlashAttention-3, designed for Hopper GPUs, achieves up to 75%…
-
NVIDIA open-sources cuDNN kernels after 12 years, including MoE and sparse attention
NVIDIA has open-sourced parts of its cuDNN library, a significant move after 12 years of it being closed-source. This release includes over 20 Mixture-of-Experts (MoE) kernels and NSA sparse attention kernels. The codeb…