12 citations · 25 across the 6 of their papers we have counts for
6 papers
Axe: A Simple Unified Layout Abstraction for Machine Learning Compilers
Bohan Hou, Hongyi Jin, Guanjie Wang +7
Scaling modern deep learning workloads demands coordinated placement of data and compute across device meshes, memory hierarchies, and heterogeneous accelerators. We present Axe La…
Gecko: An Efficient Neural Architecture Inherently Processing Sequences with Arbitrary Lengths
Xuezhe Ma, Shicheng Wen, Linghao Jin +11
Designing a unified neural network to efficiently and inherently process sequential data with arbitrary lengths is a central and challenging problem in sequence modeling. The desig…
WebLLM: A High-Performance In-Browser LLM Inference Engine
Charlie F. Ruan, Yucheng Qin, Akaash R. Parthasarathy +11
Advancements in large language models (LLMs) have unlocked remarkable capabilities. While deploying these models typically requires server-grade GPUs and cloud-based inference, the…
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Ruihang Lai, Junru Shao, Siyuan Feng +16
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the deman…
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Siyuan Feng, Bohan Hou, Hongyi Jin +8
Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimen…
Tensor Program Optimization with Probabilistic Programs
Junru Shao, Xiyou Zhou, Siyuan Feng +7
Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search spa…