Emmtrix ONNX-to-C Code Generator for Edge AI Deployment
Hi HN, we wanted to share our open source ONNX-to-C code generator. It translates ONNX models into C code for deployment on embedded systems. We developed it for use with emmtrix Code Vectorizer (https://www.emmtrix.com/tools/emmtrix-code-vectorizer) which optimizes the generated code for various embedded architectures. The ONNX-to-C code generator can however also be used standalone to generate plain C code. In contrast to many other tools, it makes deployment trivial since the generated code is fully standalone and no additional runtime is required.
Does the same job
all alternatives →- N5NN-512 – Generate standalone C code for neural nets2020 · nn-512.com · ▲111
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- ZAZant – A TinyML SDK in Zig2025 · github.com · ▲7
Hey HN, We’re excited to announce Zant v0.1, an open-source TinyML SDK built in Zig, designed to optimize and deploy neural networks on resource-constrained devices. Unlike existing solutions, Zant focuses on performance, portability, and ease of integration, making it a strong alternative for anyone working on Edge AI and embedded ML. Why Zant? Most TinyML frameworks are either too high-level (requiring bloated runtimes) or too low-level (requiring extensive manual optimization). Zant bridges the gap by offering: - A lightweight but powerful code generation system to translate ML models…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com

