Why Python Struggles in Safety-Critical Embedded Systems

What are Embedded Systems?

Before diving into Python's role, let's establish what embedded systems actually are. An embedded system is a specialized computer designed to perform specific tasks within a larger system. Unlike your laptop or smartphone, which runs many different applications, an embedded system typically does one job and does it reliably for years. You'll find embedded systems everywhere: in your car's braking system, your microwave, medical devices, and industrial machinery.

Traditionally, these systems have been programmed in low-level languages like C or assembly, which give developers precise control over hardware and memory. But Python, known for its simplicity and readability, has started making inroads into this space. The question for engineers is: when does Python make sense, and when should you stick with traditional languages?

Where Python Excels in Embedded Systems

Python has found genuine value in several embedded contexts, particularly through specialized implementations like MicroPython and CircuitPython, which are optimized versions designed to run on resource-constrained microcontrollers.

1. Rapid Prototyping and Proof of Concept

Python's greatest strength in embedded development is speed of development. When engineers need to quickly test an idea - such as reading data from a sensor, controlling an LED display, or prototyping a smart home device - Python allows them to write functional code in minutes rather than hours. This is invaluable during early product development when requirements are still evolving.

2. Internet of Things (IoT) Applications

For IoT devices that collect and transmit data, such as environmental sensors, smart agriculture monitors, or home automation systems, Python often provides an excellent balance of capability and simplicity. Devices like the ESP32 or Raspberry Pi Pico can run MicroPython and connect easily to cloud services, making them ideal for applications where network communication matters more than split-second timing.

3. Education and Maker Projects

CircuitPython has made embedded programming accessible to students and hobbyists. Its simplified libraries and "drag-and-drop" file editing remove much of the complexity that traditionally made embedded development intimidating to beginners.

The Critical Limitations: Why Python Falls Short in Mission-Critical Systems

While Python has its place, functional safety engineers must understand its fundamental limitations, particularly for systems where failures could result in safety-critical hazards. Here's why Python is generally unsuitable for safety-critical applications:

1. Non-Deterministic Timing and the Garbage Collector

Safety-critical systems often have strict real-time requirements: certain tasks must complete within precise time windows - every single time. Python uses a feature called a "garbage collector", an automatic process, that periodically scans memory to find and clean up data the program no longer needs. While this simplifies development, the garbage collector can run at unpredictable moments and introduce sudden pauses in program execution. Research confirms that Python's garbage collector and Global Interpreter Lock (GIL) create non-deterministic behavior that makes meeting real-time deadlines extremely challenging.

In a braking system, a delay of even a few milliseconds could be catastrophic. Imagine a scenario where a driver slams the brakes to avoid a collision - the system must read the pedal sensor, calculate the required brake force, and activate the actuators within a strict time window. If Python's garbage collector decides to run its cleanup routine at that exact moment, the system could pause for several milliseconds, delaying the brake response and potentially causing the very accident it was designed to prevent.

2. Memory usage concerns

Embedded systems often operate with severely limited memory, sometimes just kilobytes. Python's dynamic typing and interpreted nature result in a larger memory footprint compared to compiled languages like C.

For example, a simple 32-bit integer in C code occupies just 4 bytes. The same integer in Python is a full object with reference counters, type metadata, and a dynamically allocated digit array. As a result, a Python integer typically consumes around 28 bytes, more than 7× larger than its C equivalent. This overhead multiplies quickly in embedded systems with tight RAM budgets, making Python far less suitable for memory-constrained environments.

3. Memory Management concerns

More critically, automatic memory management removes the precise control that safety engineers need when designing mission-critical systems.

In safety-critical software, it's common to have both safety-related and non-safety-related code running together. ISO 26262 requires "freedom from interference" - guaranteeing that non-safety code cannot corrupt or disrupt safety functions. In languages like C, engineers can explicitly partition memory, allocating separate regions for safety and non-safety components and using hardware memory protection units (MPUs) to enforce strict boundaries.

Python's garbage collector manages all memory in a shared heap, with no mechanism to isolate safety-critical data from the rest of the application. This means a bug in non-safety code - such as a logging function or user interface element - could potentially overwrite memory used by safety functions, leading to unpredictable behavior in the very code designed to prevent hazards.

4. Lack of Qualified Toolchains

Functional safety standards like ISO 26262 (automotive), IEC 61508 (industrial), and DO-178C (aerospace) require qualified toolchains - compilers and development tools that have been verified to produce correct, predictable output. Currently, no Python interpreter or runtime has been qualified for use in safety-critical systems at any integrity level.

In contrast, C compilers have decades of qualification history. More recently, the Rust programming language achieved a significant milestone when Ferrous Systems obtained ISO 26262 ASIL-D and IEC 61508 SIL-4 qualification for their Ferrocene compiler, the highest safety levels in both standards. This demonstrates the industry's direction: languages that provide memory safety with deterministic behavior are gaining ground, but Python is not among them.

When to choose which Language for embedded systems

The choice of programming language for an embedded project should align with the system's safety and performance requirements:

Use Case	Recommended Language	Rationale
Rapid Prototyping	Python	Fast Development, easy iteration
IoT Data Collection	Python or C	Depends on timing requirements
Consumer Electronics	C/C++	Balance of performance and complexity
Mission-critical systems	C, C++, Rust, or Ada	Qualified toolchains, determinism, Predictable timing, no garbage collection

A practical approach for engineering teams

For organizations working on products that span both non-critical and safety-critical components, a hybrid approach often makes sense:

1. Use Python for development tools, testing scripts, data analysis, and rapid prototyping of concepts

2. Use C, C++, or Rust for production firmware, particularly any safety-related functions

3. Maintain clear boundaries between safety-critical and non-safety-critical components

This allows teams to benefit from Python's productivity advantages during development while ensuring that the final safety-critical code meets the rigorous requirements of functional safety standards.

Conclusion

Python has earned a valuable place in the embedded systems ecosystem, particularly for prototyping, IoT applications, and educational purposes. Its readability and extensive library ecosystem make development faster and more accessible.

However, for safety-critical systems governed by standards like ISO 26262 or IEC 61508, Python's non-deterministic behavior, garbage collection, and lack of qualified toolchains make it unsuitable. In these contexts, languages like C, C++, Rust, and Ada remain the appropriate choices, offering the determinism, memory control, and certified toolchains that functional safety demands.

Understanding these trade-offs allows engineering teams to select the right tool for each job, leveraging Python's strengths where appropriate while respecting its limitations in safety-critical contexts.