Sharath Challapalli
Design Verification Engineer
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolSharath Challapalli is a Design Verification Engineer with 10 years of experience, currently driving verification efforts at Intel from Austin, Texas. He blends low-level hardware expertise—SystemVerilog/UVM, Verilog/VHDL, UPF low-power design and Synopsys flows—with strong software skills in C/C++ and Python to bridge RTL, simulation and tool-driven signoff. His background includes hands-on FPGA layout and EM/IR analysis at Xilinx and MATLAB/ModelSim co-simulation of control systems for BLDC actuators, giving him a practical eye for timing, power and silicon realities. Sharath also contributes to NLP model engineering on GitHub, integrating attention-based MatchLSTM layers into IntelLabs’ nlp-architect, demonstrating an uncommon mix of verification rigor and ML coding. He holds a master’s from Texas A&M and a bachelor’s from IIT Jodhpur, and is known for translating complex architecture constraints into reproducible verification strategies.
10 years of coding experience
1 year of employment as a software developer
Bachelor’s Degree, Biologically Inspired System Science(EE), Bachelor’s Degree, Biologically Inspired System Science(EE) at IIT JodhpurMaster's degree, Electrical, Electronics and Communications Engineering, Master's degree, Electrical, Electronics and Communications Engineering at Texas A&M University
Telugu, English, Hindi
Stackoverflow
1reputation
0reached
0answers
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Github Skills (8)
pytorch10
nlp10
deep-learning10
tensorflow10
n9
rnn-model9
transformers8
bert8
Programming languages (3)
Github contributions (5)
IntelLabs/nlp-architect
Apr 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:
ML Engineer
ML EngineerContributions:32 commits in 6 months
Contributions summary:Sharath contributed to the `intellabs/nlp-architect` repository by implementing and modifying layers for reading comprehension models. They specifically focused on integrating the MatchLSTMCell with attention mechanisms, as evidenced by the code changes in `reading_comprehension/ngraph_implementation/layers.py`. The commits also involve initial setup and updates related to reading comprehension models utilizing TensorFlow.
nlunatural-language-understandingbertlanguage-processingstate-of-the-art
NervanaSystems/ngraph-tensorflow-bridge
Mar 2018 - May 2018
Contributions:9 PRs, 9 pushes, 7 branches in 1 month
bridgetensorflowngraph
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