Shlomi Regev is a Staff Software Engineer based in Stockholm with five years of professional experience, currently building scalable ML and embedded systems at Google. He brings deep hands-on expertise in deploying machine learning to resource-constrained targets, having contributed performance and memory optimizations to the widely used TensorFlow Lite Micro project—including kernel improvements and Xtensa-specific enhancements. Comfortable bridging low-level kernel work with build-system and architecture-specific tuning, he focuses on making ML models practical on microcontrollers and DSPs. Educated at Tel Aviv University and UCLA, he pairs solid academic grounding with pragmatic engineering that favors efficient, production-ready solutions.
Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).
Role in this project:
ML Engineer
Contributions:32 reviews, 17 commits, 48 PRs in 1 year 7 months
Contributions summary:Shlomi primarily contributed to optimizing and expanding the capabilities of the TensorFlow Lite Micro framework, particularly focusing on embedded machine learning model deployment. Their work involved modifying kernel implementations, including those for Flexbuffer and the Cast operator, to improve performance and memory usage. They also addressed build script improvements and added support for Xtensa architecture specific optimizations, demonstrating a strong understanding of low-power, resource-constrained environments.
Contributions:102 pushes, 42 branches in 3 years 3 months
tensorflow-litelitetensorflowmicrocontrollers
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