54k citations · 60.1k across the 24 of their papers we have counts for
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Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
Byungsoo Jeon, Sunghyun Park, Peiyuan Liao +3
The strong demand for efficient and performant deployment of Deep Learning (DL) applications prompts the rapid development of a rich DL ecosystem. To keep up with this fast advance…
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…
Automated Backend-Aware Post-Training Quantization
Ziheng Jiang, Animesh Jain, Andrew Liu +4
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment. However, different hardware backends such as x86 CPU, N…