Search Results for author: Vin Sharma

Found 3 papers, 0 papers with code

Bring Your Own Codegen to Deep Learning Compiler

no code implementations3 May 2021 Zhi Chen, Cody Hao Yu, Trevor Morris, Jorn Tuyls, Yi-Hsiang Lai, Jared Roesch, Elliott Delaye, Vin Sharma, Yida Wang

Deep neural networks (DNNs) have been ubiquitously applied in many applications, and accelerators are emerged as an enabler to support the fast and efficient inference tasks of these applications.

Code Generation

Efficient Execution of Quantized Deep Learning Models: A Compiler Approach

no code implementations18 Jun 2020 Animesh Jain, Shoubhik Bhattacharya, Masahiro Masuda, Vin Sharma, Yida Wang

A deep learning compiler such as Apache TVM can enable the efficient execution of model from various frameworks on various targets.


Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference

no code implementations4 Jun 2020 Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang

Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.

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