Chinnikrishna Kothapalli
Senior Performance Architect at Microsoft
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolChinnikrishna Kothapalli is a Senior Performance Architect based in the San Francisco Bay Area with eight years of experience at the intersection of AI hardware and deep learning software. He has driven performance architecture and simulator development for AI workloads at Microsoft and Intel, including FPGA acceleration, SoC integration, and HW design-space exploration for deep learning. His research-forward approach blends hands-on model development—especially in NLP, multimodal (NLP+RL) and linguistically grounded models—with interests in causality and AGI, informing pragmatic hardware choices. He contributes to open-source NLP tooling and documentation (notably updates to IntelLabs/nlp-architect), reflecting both systems-level insight and clear communication. Colleagues rely on him to translate research questions into measurable workload projections and hardware optimizations that accelerate real-world DL systems.
8 years of coding experience
6 years of employment as a software developer
Jawaharlal Nehru Technological University HyderabadSri Gowthami Public SchoolMaster's degree, Electrical and Electronics Engineering, Master's degree, Electrical and Electronics Engineering at Portland State UniversityNarayana
Hindi, Telugu, English
Github Skills (7)
rs10
restructuredtext10
documentation10
pytorch6
transformers6
deep-learning6
tensorflow6
Programming languages (1)
Github contributions (5)
IntelLabs/nlp-architect
May 2018 - Oct 2018
A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks
Role in this project:
Technical Writer
Technical WriterContributions:2 releases, 28 commits, 2 pushes in 5 months
Contributions summary:Chinnikrishna's commits primarily focus on updating and modifying documentation files within the `doc/source` directory. These changes involve removing dependencies, adding or modifying code snippets and filepaths, and rewriting sections of existing documentation. The user demonstrates an understanding of the project's architecture and different models. The updates include restructuring existing documentation for improved clarity and organization.
nlunatural-language-understandingbertlanguage-processingstate-of-the-art
NervanaSystems/ngraph-python
Jul 2017 - Jan 2018
Original Python version of Intel® Nervana™ Graph
Contributions:15 commits in 5 months
pythoninteloneapigraphpython-version
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.