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:8 reviews, 5 commits, 9 PRs in 2 months
Contributions summary:Jack primarily contributed to the implementation and optimization of the TensorFlow Lite Micro framework, specifically for embedded systems. They added support for Xtensa kernels, fixed build errors, and incorporated new operations like AddExp. Furthermore, the user implemented optimizations for CONV2D and DEPTHWISE_CONV2D for the Vision P6 platform, indicating a focus on performance enhancement for specific hardware. They also co-authored commits, showcasing collaborative development within the project.
embeddedmicrocontrollermlmodel
TensorFlow Lite for Microcontrollers
Contributions:14 pushes, 8 branches in 3 months
tensorflow-litelitetensorflowmicrocontrollers