How AI Is Changing the Landscape of Embedded Design

 Source: btiger/stock.adobe.com; generated with AI

Artificial intelligence (AI) is redefining embedded systems at every level, from their design to the capabilities they enable.

This article explores three key areas where AI is making a significant impact: software development, hardware design, and AI-powered applications. It examines how AI is impacting each area, highlights leading-edge tools, and points out design principles critical to success in this new design paradigm.

AI-Powered Embedded Development: Smarter, More Efficient Coding

Software tools have seen some of the most rapid advancements in AI. This is unsurprising, as large language models (LLMs) are a natural fit for text-heavy codebases. But it’s not just about code generation: AI also plays a vital role in documentation, visualization, and testing. Let’s take a closer look at the trends in each area.

AI Coding Assistants for Embedded Systems

Initially, AI coding assistants helped with general-purpose programming, but they have come a long way. Now, they are becoming an asset for embedded systems too. Tools like GitHub Copilot and Amazon CodeWhisperer can suggest code that fits microcontroller-based development, making them ideal for tasks like the following:

•    Peripheral initialization and configuration specific to microcontroller architectures
•    Interrupt handlers optimized for event-driven systems
•    Communication protocols to facilitate data exchange between integrated components

This evolution means that AI coding tools are now capable of assisting with the low-level, hardware-centric challenges of embedded development. However, these benefits only materialize when the tools are used correctly. Developers should take care to:

•    understand the target hardware—AI may not understand the nuances of power management, memory constraints, and other system particulars;
•    validate generated code—AI often produces syntactically correct code that may not always function as intended; and
•    be mindful of dependencies—AI-generated code may introduce libraries that are unsuitable for an embedded environment.

A recurring theme in this article is that AI works best in the hands of deeply knowledgeable designers. In other words, AI should enhance human engineering skills, not replace them.

Automated Documentation and Visual Tools

Documentation has traditionally been one of the most time-consuming and frequently overlooked aspects of embedded development. Now, AI-powered tools are transforming how technical references are created, maintained, and visualized:

•    Documentation: Tools like Doxygen extract structured comments from source code to generate API documentation that stays synchronized with code updates.
•    Explanation: Assistants such as GitHub Copilot Chat can analyze complex functions and generate summaries that make code easier to understand.
•    Visualization: Platforms like Mermaid Chart can help create visuals, such as timing diagrams for protocols, helping bridge the gap between hardware and software teams.

One caveat is that these tools require proper configuration and inputs to function effectively. For example, Doxygen needs specific comment formatting conventions to work correctly.
Context is another key consideration. Automated tools may miss nuances that would be obvious to experienced engineers. Therefore, it is essential to carefully review generated documentation, especially in safety-critical systems where documentation errors could have significant consequences.

Enhanced Testing Frameworks

AI is also significantly boosting code testing. Frameworks like Eggplant Test Automation enable advanced hardware-in-the-loop (HIL) simulation, allowing developers to generate more diverse test cases.¹  With a broader suite of tests, these tools can catch power consumption, resource utilization, and race condition issues that might be missed with traditional testing methods. For example, the tools can identify problems that might manifest only in specific timing conditions. Testers can also dynamically adjust parameters in real time, continually refining their code in the process.

Though they are sophisticated, these frameworks lack human creativity and intuition. They tend to struggle with more nuanced concepts, such as zero-carbon design.²  As such, they require human oversight and input to maximize their effectiveness.

But the tools are only as good as their training data. Without high-quality data, their accuracy and reliability suffer.

AI-Driven Design: Streamlined from Components to Prototyping

While software tools have seen rapid advancement, AI has arguably transformed hardware design even more. Designers can now access AI-powered tools for everything from component selection to simulation and testing. In many cases, these tools replace manual processes, giving designers opportunities for massive improvements in time to market and design optimization.

Advanced Simulation and Testing
AI offers considerable benefits in analog circuit simulation. Traditional SPICE simulators require engineers to manually set parameters—a complex and time-consuming process. In contrast, tools like Synopsys PrimeSim and Siemens Solido Design Environment automatically configure simulations and predict potential issues. In addition to helping identify critical test cases, the tools can optimize power delivery networks and suggest component values that improve overall system efficiency.

For printed circuit board (PCB) routing optimization, tools like Flux and DeepPCB leverage deep learning to complement human design expertise. These systems can evaluate numerous configurations while gauging the impact of trace placement on signal quality, heat dissipation, and power delivery.

Keep in mind that analog simulation is notoriously finicky. For example, slight variations in component values or operating conditions can lead to large changes in circuit behavior. Thus, engineers need a solid foundation in analog design to use these tools successfully.

AI for Chip Design Optimization
AI is also revolutionizing FPGA and ASIC design, exemplified by tools such as Synopsys DSO.ai. Among other capabilities, this tool can optimize for multiple objectives simultaneously, allowing designers to rapidly find the most effective design.³  According to Synopsys, DSO.ai has the potential to triple productivity, substantially reduce die size, and decrease power consumption by up to 15 percent.⁴ 

A potential drawback of these tools is interpretability. AI-generated FPGA and ASIC design may be difficult to analyze or modify manually, especially in cases where a non-traditional approach is suggested.

Rapid Prototyping and Manufacturing Integration
AI-powered tools are also streamlining prototyping and manufacturing. Solutions like Autodesk Fusion 360 use machine learning (ML) to optimize component placement, signal integrity, and thermal performance, helping engineers iterate faster. Engineers can quickly adjust designs, run simulations, and explore alternatives, reducing the time required to move from concept to final product.

AI can also improve manufacturing processes by ensuring the performance and efficiency of operational equipment align with automated production workflows. Edge AI development frameworks are maturing to enable widespread deployment of predictive maintenance capabilities that optimize equipment efficiency, quality control, and operational costs.

Designing for Edge AI: Bringing Predictive Maintenance to Life

While AI-driven tools accelerate development, AI also enables new design possibilities. One of the most mature applications is predictive maintenance, where it has been deployed in the field for several years. As AI models become optimized for microcontrollers and edge devices, embedded engineers can now deploy intelligent features directly on hardware that once lacked the power to support such capabilities.

Resource-Efficient AI Platforms for Edge Devices
Traditionally, embedded systems and edge devices had limited ability to support AI algorithms. Most ML models simply were not designed for such resource-constrained operating environments. Embedded AI platforms like TensorFlow Lite for Microcontrollers and Edge Impulse have changed this, offering several key advantages:

•    Ability to run ML models on devices with limited resources (starting from about 50KB of memory)
•    Comprehensive tools for quantizing and optimizing models
•    Simplified workflows for selecting, training, and tailoring models to specific microcontroller architectures

Hardware is also evolving. A growing range of microprocessors and microcontrollers are adding accelerators that make running AI on relatively low-end systems possible.

Optimizing Models with AutoML
Engineers can further streamline model development by using automated machine learning (AutoML) tools like AutoKeras. These solutions allow quick optimization of a model’s architecture and weight based on hardware capabilities and application requirements. The trade-off is that one needs expertise not only in a system’s target hardware but also in AI and ML, as AutoML tools often require iterative refinement.

Training data quality presents another challenge for edge AI implementation. One common issue is overfitting, which can occur if a model is trained on a data set that is too small. Ironically, performance can also suffer if the model or dataset is too complex. Finding the right balance is as much an art as a science, so it is wise to seek the input of an experienced AI expert when beginning your model-building journey.

Predictive Maintenance Applications
For industrial applications, predictive maintenance is where edge AI truly shines. Most modern industrial systems contain many sensors, covering everything from vibration and current draw to acoustic signatures. A predictive maintenance algorithm can ingest all this information and analyze it for any patterns that might indicate impending equipment failure.

The AI can then alert an operator about the problem, at which point they can repair or replace faulty components.

This capability becomes even more powerful when combined with federated learning—a technique that trains a model on distributed, localized datasets, sending model updates to a central server. This approach offers several benefits for industrial implementations:

•    Enhanced data privacy and security by keeping raw data local
•    Improved regulatory compliance through reduced data transmission
•    Lower operational costs due to decreased data transfer requirements

The combination of edge processing and federated learning creates a continuously improving maintenance system that becomes more accurate over time without compromising security or efficiency.

An Intelligent Future for Design and Development

AI has fundamentally changed electronics design, enabling greater efficiency and productivity from development to production. From AI-powered coding assistants and documentation tools to intelligent PCB design optimization and resource-efficient edge platforms, the technology is reshaping workflows across the entire embedded systems lifecycle.

However, AI is not something an organization can carelessly embrace. Success depends on knowing what tools to use and understanding their limitations. The most effective implementations recognize where AI excels—handling repetitive tasks, exploring vast design spaces, and identifying non-obvious patterns—and where human expertise remains irreplaceable. AI will always be at its best when paired with human intelligence, not replacing the engineer but expanding what’s possible.

¹https://www.keysight.com/us/en/assets/3124-1343/case-studies/Major-Automotive-Manufacturer-Chooses-Keysight-Eggplant.pdf
 ²https://www.cundall.com/ideas/blog/the-pros-and-cons-of-ai-in-design
 ³https://www.synopsys.com/ai/ai-powered-eda/dso-ai.html
  ⁴https://aws.amazon.com/blogs/apn/boost-chip-design-with-ai-how-synopsys-dso-ai-on-aws-delivers-lower-power-and-faster-time-to-market/