10 citations · 16 across the 4 of their papers we have counts for
4 papers
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,…
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Siyuan Chen, Pratik Fegade, Tianqi Chen +2
Batching has a fundamental influence on the efficiency of deep neural network (DNN) execution. However, for dynamic DNNs, efficient batching is particularly challenging as the data…
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
There is often variation in the shape and size of input data used for deep learning. In many cases, such data can be represented using tensors with non-uniform shapes, or ragged te…
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…