Nishant Patel
Deep Learning Compiler Engineer at Intel Corporation
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolNishant Patel is a Deep Learning Compiler Engineer with nine years of experience optimizing ML and AI stacks for Intel hardware and working on the MLIR ecosystem. He is proficient in C, C++ and Python and has driven backend improvements in high-profile open-source projects like Intel's nGraph, adding MKLDNN-backed operators, bfloat16 support, and operator fusion to boost runtime performance. At Intel he progressed from graphics driver validation to binary translation and now focuses on compiler- and runtime-level performance for state-of-the-art models. Based in San Francisco, he blends low-level systems expertise with practical ML insights to make models run faster and more efficiently on real silicon. Notably, his contributions include implementing core evaluate functions and enabling optimized primitives that are subtle but materially improve inference throughput.
9 years of coding experience
7 years of employment as a software developer
Master of Science (M.S.), Computer Science, Master of Science (M.S.), Computer Science at Indiana University BloomingtonBachelor of Engineering (B.E.), Computer Engineering, Bachelor of Engineering (B.E.), Computer Engineering at University of Mumbai
English, Hindi
Github Skills (9)
c-language10
cprogramming-language10
deep-learning9
compiler-design8
optimisation7
performance-optimization7
optmization7
optimization7
python5
Programming languages (4)
Github contributions (5)
NervanaSystems/ngraph
Mar 2018 - May 2020
nGraph - open source C++ library, compiler and runtime for Deep Learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:204 commits, 92 PRs, 469 pushes in 2 years 2 months
Contributions summary:Nishant primarily contributed to the development of the nGraph deep learning compiler, runtime, and C++ library. Their work focused on improving the codebase and integrating MKLDNN with various components. The user added support for several operators, including Concat, convolution, and transpose with MKLDNN, and enhanced the backend by supporting new element types, such as bfloat16, and providing better operator fusion capabilities. In addition, they implemented the evaluate function for gather.
inference-enginecppc-librarydeep-learningtvm
nbpatel/llvm-project
Mar 2023 - Feb 2025
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Note: the repository does not accept github pull requests at this moment. Please submit your patches at http://reviews.llvm.org.
Contributions:2 PRs, 49 pushes, 13 branches in 1 year 11 months
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