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ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
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, exiting early out of deep models and so on.
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Further, batching puts strict restrictions on memory adjacency and can lead to high data movement costs.
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
There is often variation in the shape and size of input data used for deep learning.
Cortex: A Compiler for Recursive Deep Learning Models
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 found in vendor libraries.
