33 citations · 68 across the 16 of their papers we have counts for
18 papers
Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation
Hermann Kumbong, Ishan S. Khare, Iddah Mlauzi +6
Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as inference becomes a dominant compu…
SyclKittens: A Tile Programming Model for Programmers and Coding Agents on Intel GPUs
Yehong Jiang, Sheng Chen, Fangwen Fu +4
New AI accelerators arrive before the kernels that make them fast, because peak kernel performance requires architecture-specific expertise in operand pipelines and data-movement t…
ThunderAgent: A Simple, Fast and Program-Aware Agentic Inference System
Hao Kang, Ziyang Li, Weili Xu +7
Large language models(LLMs) are now used to power complex multi-turn agentic workflows. Existing systems run agentic inference by loosely assembling isolated components: an LLM inf…
ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels
Stuart H. Sul, Simran Arora, Benjamin F. Spector +1
Inter-GPU communication has become a major bottleneck for modern AI workloads as models scale and improvements in hardware compute throughput outpace improvements in interconnect b…
HipKittens: Fast and Furious AMD Kernels
William Hu, Drew Wadsworth, Sean Siddens +6
AMD GPUs offer state-of-the-art compute and memory bandwidth; however, peak performance AMD kernels are written in raw assembly. To address the difficulty of mapping AI algorithms…
Cartridges: Lightweight and general-purpose long context representations via self-study
Sabri Eyuboglu, Ryan Ehrlich, Simran Arora +8
Large language models are often used to answer queries grounded in large text corpora (e.g. codebases, legal documents, or chat histories) by placing the entire corpus in the conte…
Towards Learning High-Precision Least Squares Algorithms with Sequence Models
Jerry Liu, Jessica Grogan, Owen Dugan +4
This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of least squares. Our goal is to inheri…
KernelBench: Can LLMs Write Efficient GPU Kernels?
Anne Ouyang, Simon Guo, Simran Arora +4
Efficient GPU kernels are crucial for building performant machine learning architectures, but writing them is a time-consuming challenge that requires significant expertise; theref…
ThunderKittens: Simple, Fast, and Adorable AI Kernels
Benjamin F. Spector, Simran Arora, Aaryan Singhal +2
The challenge of mapping AI architectures to GPU hardware is creating a critical bottleneck in AI progress. Despite substantial efforts, hand-written custom kernels fail to meet th…
LoLCATs: On Low-Rank Linearizing of Large Language Models
Michael Zhang, Simran Arora, Rahul Chalamala +5
Recent works show we can linearize large language models (LLMs) -- swapping the quadratic attentions of popular Transformer-based LLMs with subquadratic analogs, such as linear att…
Just read twice: closing the recall gap for recurrent language models
Simran Arora, Aman Timalsina, Aaryan Singhal +6
Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use…
Simple linear attention language models balance the recall-throughput tradeoff
Simran Arora, Sabri Eyuboglu, Michael Zhang +6
Recent work has shown that attention-based language models excel at recall, the ability to ground generations in tokens previously seen in context. However, the efficiency of atten…
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT
Jon Saad-Falcon, Daniel Y. Fu, Simran Arora +2
Retrieval pipelines-an integral component of many machine learning systems-perform poorly in domains where documents are long (e.g., 10K tokens or more) and where identifying the r…
Zoology: Measuring and Improving Recall in Efficient Language Models
Simran Arora, Sabri Eyuboglu, Aman Timalsina +5
Attention-free language models that combine gating and convolutions are growing in popularity due to their efficiency and increasingly competitive performance. To better understand…
RELIC: Investigating Large Language Model Responses using Self-Consistency
Furui Cheng, Vilém Zouhar, Simran Arora +3
Large Language Models (LLMs) are notorious for blending fact with fiction and generating non-factual content, known as hallucinations. To address this challenge, we propose an inte…
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
Daniel Y. Fu, Simran Arora, Jessica Grogan +7
Machine learning models are increasingly being scaled in both sequence length and model dimension to reach longer contexts and better performance. However, existing architectures s…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…
Can Foundation Models Wrangle Your Data?
Avanika Narayan, Ines Chami, Laurel Orr +2
Foundation Models (FMs) are models trained on large corpora of data that, at very large scale, can generalize to new tasks without any task-specific finetuning. As these models con…