At present, many chip manufacturers are vigorously promoting their own dedicated IDEs. However, there is a common situation, that is, these tools are often unable to seamlessly integrate AI development and key security update processes into the core.
The Current State of Embedded Development Tools
There are now many integrated development environments for specific chips in the market, such as ST's STM32CubeIDE and Renesas' e² studio. These tools generally deeply optimize support for their own microcontrollers, covering underlying drivers and peripheral configuration. They can cooperate with general IDEs such as IAR Embedded Workbench or integrate plug-ins, trying to provide developers with one-stop solutions. However, this integration often remains at the basic functional level.
The integration dilemma of AI workflows
Although AI has become a hot topic in the embedded field, there are very few IDEs that truly natively support AI model development, optimization, and deployment. Developers often have to perform operations manually, first using TensorFlow or PyTorch on a PC to train the model, then using third-party tools to perform quantification and pruning, and finally integrating the model into an embedded project with great difficulty. Such a fragmented process increases complexity and the risk of errors, and is inconsistent with the "simplified development" vision promoted by the manufacturer.
Safety and automation needs are marginalized
For modern embedded devices, secure boot and reliable OT A update mechanisms have become essential. However, the reality is that many IDEs provide these key functions as independent SDKs or add-ons, rather than as an integral part of the core development workflow. This means that developers have to spend a lot of time writing scripts and integrating frameworks to ensure security and update repeatability. This greatly reduces the degree of automation.
Manufacturers’ responses and attempts
The leading manufacturers that were the first to notice these problems have already taken action. For example, CrossCore® Embedded Studio launched by ADI focuses on hardware abstraction and automation tools, aiming to simplify the entire process from concept to deployment. Jason, the software leader of this tool, said that this tool can provide compatibility analysis reports and model optimization suggestions to help AI models adapt to limited hardware resources. Such efforts are in the right direction, but its popularity and depth still need to be further seen.
Unified experience and AI-empowered IDE
This part of the improvement focuses on improving the consistency of the development experience. The updated tools begin to support a wider range of devices and applications in multi-core environments. The unified configuration interface is intended to reduce the inherent complexity of the hardware ecosystem. For more forward-looking exploration, it uses AI technology to implement the optimization of the IDE itself. Microsoft has given Visual Studio has formulated a clear AI roadmap, with the goal of creating an "AI agent" that can understand the context of the code and give intelligent and appropriate suggestions. This may represent a general trend in the evolution of IDEs in the future.
Future challenges and directions
The real challenge lies in how to break down the barriers between different tool chains to build a truly developer-centric unified platform with embedded security and AI workflows. This really requires deeper collaboration between chip manufacturers, software tool providers and the open source community. It is no longer enough to just optimize the performance of your own hardware or launch a separate AI toolkit. Ultimately, turning security, reproducibility, automation, and AI integration into out-of-the-box basic services is the core of the competition for the next generation of embedded IDEs.
For those embedded engineers who have to fight fiercely with complex tool chains every day, which feature do you think is most urgently needed to be natively supported by IDEs? Is it a function that enables one-click deployment of AI models, or is it a function that has a flawless and secure update process? Feel free to share the pain points you actually face in the comment area.
