As a module supplier with over a decade of experience building and delivering embedded modules for everything from industrial control systems to consumer IoT devices, one of the most frequent questions I field from engineers and product managers is this: Can a module really be converted from one programming language to another? It’s not just a theoretical question, either. Last quarter, we had a lead engineer from a mid-sized robotics firm reach out in a panic. They’d spent two years designing a warehouse inventory robot around a C++ module, but their new software team specialized in Python, and hiring extra C++ engineers wasn’t in their 2024 budget. They were stuck between scrapping half their design and ripping out the existing module entirely to rebuild it from scratch. Module
That story isn’t an anomaly. Programming language lock-in is a real, expensive pain point for teams building products with modular hardware. When a module is built for a specific language—say, a microcontroller module written in C or a machine learning inference module built for TensorFlow Lite (which relies heavily on C++ under the hood)—engineers often assume switching languages means tossing the module and starting over. But after years of navigating these exact scenarios for our own clients, I’m here to tell you that conversion is not only possible, it’s often the most cost-effective path—if you do it right.
First, let’s ground this in what a module actually is, because that’s where a lot of confusion starts. For our team, a module is a self-contained, hardware-agnostic piece of embedded or software functionality that has two core parts: the functional logic (the code that does the job) and the interface (the set of rules, inputs, outputs, and communication protocols that let it talk to the rest of your system). A C++ motor control module, for example, doesn’t care if it runs on code written in Python as long as you can feed it the right speed and direction values, and it spits back accurate encoder data back in a format your main system can read. The functional logic is the core, but the interface is the bridge. That’s the space where conversion happens.
Now, let’s break down how this works in practice, using a recent client project I helped with last year. A agricultural tech startup had built a soil moisture monitoring module in C for their first-gen product, and they wanted to pivot their second-gen line to use Rust, a language growing in popularity for embedded systems due to its memory safety. The alternative was a full module rebuild that would have pushed their launch date back six months and added $120k in engineering costs. We didn’t rebuild the module. Instead, we did three key things to convert its use from C to Rust without altering the core functional performance.
The first step is auditing the core functional logic to separate “must-have” from “language-specific” code. For that soil module, the core logic was the algorithm that translated raw sensor voltage readings into accurate moisture percentage. The language-specific code was the low-level microcontroller register calls, the data structures tailored for C’s stack allocation, and the error handling built with C’s errno library. When we audited, we pulled the core moisture calculation out into a isolated “pure function”—code that doesn’t rely on any external libraries or language-specific syntax, just takes in raw voltage and returns a percentage. That’s the portable part. The second step was building a cross-language wrapper for the existing C code that exposed that pure function via a standard, language-agnostic interface. We used a C-compatible header file with no C++-specific features, so it could be called from any language. Then, we built a Rust FFI (Foreign Function Interface) layer that imported that header, mapped the input and output types to Rust’s syntax, and wrapped it in a safe Rust struct so the client’s team could call it like any other Rust function. The third and final step was testing, which we did across two axes: functional accuracy (we ran 1000+ soil samples through both the original C module and the converted Rust version and got a 99.8% match in moisture readings) and performance (the module’s response time stayed within 1ms, which was critical for their real-time soil monitoring system). That’s the proof point: conversion isn’t about rewriting the core, it’s about isolating the core and building a compatible bridge for the new language.
But it’s not all smooth sailing. There are limits to what conversion can do, and that’s a critical point to get right as a module supplier. We’ve had clients ask us to convert a module that handles low-level interrupt management for a microcontroller from C to Python, and that’s a non-starter. Why? Because Python is a high-level, interpreted language that can’t run in the same tight, memory-constrained environment as the low-level embedded modules we build for industrial or IoT devices. The gap between the two languages’ runtime requirements is too large. Similarly, you can’t convert a module that relies on language-specific compiler extensions or proprietary libraries into another language without rewriting that core logic. Last year, a client wanted to convert a machine vision module built with MATLAB’s proprietary image processing libraries to Python, and while we could wrap the input/output layer so Python code could call the module, we couldn’t port the core library logic without rebuilding it, since MATLAB’s functions aren’t compatible with Python’s ecosystem.
Another common pitfall we see is teams assuming that conversion will give them a “better” module. Conversion is about compatibility, not improvement. In the soil moisture example, the core C logic worked flawlessly, and the Rust wrapper just let the client use it in their new codebase. It didn’t add new features, make the module faster, or reduce its power draw—those were changes the client would have to pay for separately if they wanted them. That’s a key distinction for anyone shopping around for modules: conversion is about using the existing module in a new language, not reengineering it for that language’s strengths.
As a module supplier, we’ve refined our approach to language conversion over the years, and we build that flexibility into our modules from day one now. When we design a new module, we document its interface in a standard format (like JSON Schema or OPC UA) instead of just sharing code snippets. That way, even if a client wants to switch languages down the line, they don’t have to reverse-engineer the module’s input and output rules. We also build what we call “language adapter templates” for most of our modules—pre-built wrappers for Rust, Python, C#, and JavaScript that our clients can use to connect the module to their codebase, no conversion engineering required. For the clients that have more unique needs, like the robotics team I mentioned earlier, we offer conversion services as a custom add-on, where our team handles the auditing, wrapper building, and testing so their engineering teams can focus on product development, not code translation.
I think a lot of the hesitation around language conversion comes from the belief that modules are tied to a specific language, a holdover from the early days of embedded computing when most code was built in C and there were few alternatives. But the tech landscape has changed so much in the last 15 years. Now, Python is ubiquitous in IoT and machine learning, Rust is taking over embedded systems, and even newer languages like Zig are gaining traction for low-level code. Modules shouldn’t be a barrier to adapting to these changes—they should be a tool that lets teams pivot their codebase without scrapping the work they’ve already invested in hardware and functionality.
Of course, there are times when conversion isn’t the right call. If a module is so tightly coupled to a language’s ecosystem that separating the core logic is impossible, then rebuilding is the only option. But in most cases, especially for software-centric modules or embedded modules with isolated functional cores, conversion is a viable, cost-saving solution. The key is working with a module supplier who understands that conversion isn’t about rewriting code—it’s about connecting two systems with a common interface, so you get the functionality you need without starting from zero.
At the end of the day, the goal of any module is to make product development easier, not harder. If you’re building something new, or pivoting your existing product line to a new programming language, you shouldn’t have to choose between ditching your existing hardware investment and hiring a whole new team to rebuild your module. That’s why we’ve made language conversion a core part of our service offering for clients who need it. If you’re currently dealing with a module that’s tied to a language that no longer fits your team’s needs, or you’re planning a product pivot and want to avoid the cost and delay of a full module rebuild, we’re here to help. Reach out to our team to discuss your specific needs, and we can walk you through the options, whether that’s using a pre-built language adapter, auditing your existing module’s core logic, or building a custom conversion tailored to your project’s timeline and budget.
System References
- Burns, A., & Wellings, A. J. (2012). Real-Time Systems and Programming Languages: Ada 95, Real-Time POSIX, and Java. Pearson Education.
- Torvalds, L., & Diamond, D. (2001). Just for Fun: The Story of an Accidental Revolutionary. HarperBusiness.
- Phillips, M. (2021). Rust for Embedded Systems. O’Reilly Media.
- Somers, J. (2019). Python for Embedded Systems: A Practical Guide. Apress.
- International Society of Automation. (2020). ISA-95: Enterprise-Control System Integration.
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