Some time ago, I was considering using nanoFramework to develop industrial control boards or IO boards suitable for .NET developers, which led to the following article:
Can C# nanoFramework be used to develop industrial controllers? A performance comparison between C language and C# on ESP32!
I always thought that C# would run more efficiently than Python, so I assumed that nanoFramework would be faster than MicroPython running on ESP32. However, the actual tests were quite surprising. I searched online for good articles explaining the reasons but found none. I even asked some AI models, but they didn’t provide convincing answers. After some time, I suddenly remembered DeepSeek’s absolute advantage in logical reasoning, so I decided to ask DS.
I asked:
Why is the efficiency of nanoFramework on ESP32 lower than that of MicroPython?DeepSeek responded:
On the ESP32 platform, the efficiency of the .NET **nanoFramework** is generally lower than that of **MicroPython** for several reasons:<br/>---<br/>### 1. **Differences in Runtime Architecture** - **nanoFramework** is based on a managed code framework that relies on the **CLR (Common Language Runtime)** to execute pre-compiled CIL (Common Intermediate Language) instructions. This architecture, while providing type safety and memory management (such as garbage collection), introduces additional runtime overhead, including: - **Garbage Collection Mechanism**: Automatic memory management can lead to unpredictable performance fluctuations, especially noticeable on resource-constrained ESP32. - **Intermediate Layer Abstraction**: Managed code interacts with hardware through the CLR, while MicroPython directly calls low-level hardware interfaces via its interpreter, reducing the intermediate layer. - **MicroPython** is an interpreted language that directly executes Python bytecode, and its interpreter is optimized for embedded systems, resulting in lower memory usage, with some low-level operations implemented through C extensions for higher efficiency.<br/>---<br/>### 2. **Abstraction Level of Hardware Access** - **nanoFramework**'s hardware drivers are typically implemented through high-level abstractions (such as libraries). For example, when controlling peripherals using the `nanoFramework.Iot.Device` library, multiple layers of encapsulation (like SPI/I2C protocol stacks) lead to additional performance loss. - **MicroPython**'s hardware drivers are closer to the hardware level, allowing direct manipulation of registers or hardware interfaces through the `machine` module, reducing abstraction layers and improving execution efficiency.<br/>---<br/>### 3. **Real-time Performance and Resource Management** - **nanoFramework**'s real-time performance is limited by the managed environment. For instance, garbage collection can cause task delays, and the limited memory of the ESP32 (typically 4MB to 16MB) exacerbates this issue. - **MicroPython** performs better in real-time tasks due to its simple interpreter design, with some critical operations (like interrupt handling) implemented in native code, resulting in faster response times.<br/>---<br/>### 4. **Firmware and Compilation Optimization** - **nanoFramework**'s firmware must include the complete CLR runtime and libraries, resulting in a larger size (usually over 1MB), consuming more Flash and RAM resources. For example, firmware supporting graphics drivers must additionally load display libraries, further increasing resource consumption. - **MicroPython**'s firmware is more streamlined and can optimize memory usage by selectively compiling to remove unused modules.<br/>---<br/>### 5. **Development Model and Ecosystem** - **nanoFramework** emphasizes **.NET ecosystem compatibility** (such as C# syntax and NuGet package management), but this also means it needs to accommodate more general features, sacrificing some optimization space for embedded scenarios. - **MicroPython** is specifically designed for embedded systems, focusing its ecosystem on hardware operations (like sensor drivers and low-power modes), with a lightweight toolchain (like Thonny IDE) suitable for rapid prototyping.<br/>---<br/>### Summary and Comparison of Applicable Scenarios| **Feature** | **nanoFramework** | **MicroPython** ||-------------------------|--------------------------------------------|------------------------------------------|| **Running Efficiency** | Lower (managed code overhead) | Higher (interpreter optimization + native extensions) || **Real-time Performance** | Average (affected by garbage collection) | Better (fast interrupt response) || **Hardware Abstraction Level** | High (library encapsulation) | Low (direct register operations) || **Development Convenience** | High (C# syntax + Visual Studio integration) | High (Python syntax + rapid prototyping) || **Applicable Scenarios** | Complex logic, .NET ecosystem migration projects | Rapid development, high real-time requirements in embedded applications |<br/>---<br/>### Optimization Suggestions - To improve performance in **nanoFramework**, one could: 1. Reduce managed heap memory allocation (e.g., avoid frequent object creation). 2. Use native code extensions (e.g., drive WS2812 LED strips through the ESP32's RMT module). 3. Choose lightweight firmware (e.g., versions without graphics drivers).<br/>If you need further details on specific implementations, please refer to related blogs and documentation.Indeed, it is surprising to realize that the additional runtime and garbage collection in a low-resource system like a microcontroller can consume a significant portion of the running resources, which is the reason for its slowness. Moreover, the retained high-level abstract encapsulation is undoubtedly a struggle for the already limited resources of microcontrollers.
DeepSeek is indeed impressive; it has clarified my doubts.