10 citations · 10 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation,…
Cortex: A Compiler for Recursive Deep Learning Models
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those…