Animesh Chowdhury
AI Driven ASIC Design And Implementation Automation Expert
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolAnimesh Chowdhury is an AI-driven ASIC design and implementation automation expert with a decade of experience applying machine learning to chip design, EDA, and hardware security. Currently at NXP Semiconductors after research and AI roles at Qualcomm and NYU, he bridges academic rigor from a PhD in machine learning for chip design with hands-on engineering across synthesis, verification, and flow automation. His background includes winning state-of-the-art software testing competitions with VeriFuzz and contributing backend integrations to the widely used BenchExec benchmarking framework, demonstrating a knack for turning research prototypes into reliable tooling. He has deep domain experience in logic synthesis, hardware Trojan detection, and RTL PPA prediction, and has repeatedly combined static analysis, symbolic methods, and evolutionary fuzzing to improve correctness and coverage. Based in San Diego, he brings a rare mix of EDA internals, production SoC benchmarking, and ML-driven optimization to scale ASIC implementation productivity. Colleagues rely on him to translate complex verification and security research into practical, automated flows that reduce false positives and speed time-to-silicon.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Machine learning for chip design and EDA, Doctor of Philosophy - PhD, Machine learning for chip design and EDA at New York UniversityMaster's Degree, Computer Science, First Class with Distinction, Master's Degree, Computer Science, First Class with Distinction at Indian Statistical Institute, Kolkata
English, Hindi, Bengali
Stackoverflow
31reputation
1kreached
0answers
2questions
Github Skills (19)
tooling10
integrate10
python10
integrations10
testing9
benchmark9
measurement9
performance-measurement9
resource-management9
benchmarking9
google-benchmark8
benchmarkjs8
benchmarkdotnet8
microbenchmark8
jquery-ui6
Programming languages (6)
Github contributions (5)
sosy-lab/benchexec
Dec 2017 - Jan 2019
BenchExec: A Framework for Reliable Benchmarking and Resource Measurement
Role in this project:
Back-end Developer & Testing Engineer
Back-end Developer & Testing EngineerContributions:7 commits, 8 PRs, 10 comments in 1 year
Contributions summary:Animesh focused on implementing and refining tool integration within the BenchExec framework, specifically for the VeriFuzz tool. Their contributions involved creating and updating the `verifuzz.py` tool configuration, including defining required paths, adding falsification keywords, incorporating versioning, and adapting the module to handle different result types such as coverage goals. The changes directly impacted how BenchExec interacts with and interprets VeriFuzz's outputs.
cgroupsbenchmarkingpythonlinuxbenchmark-framework
NYU-MLDA/OpenABC
Aug 2021 - Jul 2022
OpenABC-D is a large-scale labeled dataset generated by synthesizing open source hardware IPs. This dataset can be used for various graph level prediction problems in chip design.
Contributions:1 release, 1 review, 65 commits in 11 months
benchmarksboolean-optimizationbulls-eyecircuit-benchmarksdatasets
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