Introducing Archimedes A Python toolkit for hardware engineering By Jared Callaham • 6 Oct 2025 A great engineer (controls being no exception) has to be part hacker, part master craftsman. You have to be a hacker because things rarely “just work” in the real world without a little… creativity. But you can’t only be a hacker; developing complex systems in aerospace, automotive, robotics, and similar industries demands a disciplined, systematic approach. You need tools that let you iterate fast and maintain a methodical workflow where changes are version-controlled, algorithms are tested systematically, and deployment is repeatable. Modern deep learning frameworks solved this years ago — you can develop in PyTorch or JAX and deploy anywhere. But those tools were built for neural net models, GPUs, and cloud deployments, not dynamics models, MCUs, and HIL testing. That’s where Archimedes comes in; what PyTorch did for ML deployment, Archimedes aims to do for control systems. The goal is to build an open-source “PyTorch for hardware” that gives you the productivity of Python with the deployability of C. In short, Archimedes is a Python framework that lets you develop and analyze algorithms in NumPy and automatically generate optimized C code for embedded systems. For instance, you can write a physics model in Python, calibrate it with data, use the model to design and simulate control logic, validate with simple hardware-in-the-loop (HIL) testing, and deploy with confidence: This is one workflow you might use with Archimedes (specifically, the one from the hardware deployment tutorial), but it’s designed to be flexible, so you’re free to build up whatever workflow suits your style and application best. The Linchpin: Python → C Code Generation Archimedes started with the question, “What would you need to actually do practical control systems development in Python?” As a high-level language, it’s hard to beat Python on design principles like progressive disclosure, flexibi...
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