ai-techsystems/deepC
vendor independent TinyML deep learning library, compiler and inference framework microcomputers and micro-controllers
This project helps embedded systems developers bring deep learning capabilities to tiny, low-power devices like microcontrollers and IoT sensors. It takes your trained deep learning models, typically in ONNX format, and compiles them into highly optimized executables that can run efficiently on resource-constrained hardware. The end-user is an embedded systems engineer or an IoT device developer looking to add intelligence directly to edge devices.
602 stars. No commits in the last 6 months.
Use this if you need to run deep learning models directly on microcontrollers, Raspberry Pis, or other small form-factor embedded systems without relying on cloud processing or powerful edge servers.
Not ideal if your application runs on powerful servers, desktops, or mobile phones with ample computational resources, as the primary benefit is optimization for extreme resource constraints.
Stars
602
Forks
92
Language
C++
License
Apache-2.0
Category
Last pushed
Jul 22, 2025
Commits (30d)
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