STMicroelectronics is expanding its STM32 microcontroller (MCU) roadmap with multicore devices, hardware accelerators, and AI-assisted development tools as it positions the platform for growing cloud AI and physical AI markets.
“There are two rapidly growing markets today, cloud AI and physical AI, and it is our duty at ST to make sure that the STM32 platform will be the king of the microcontroller market in cloud AI and physical AI,” said Remi El-Ouazzane, president, microcontrollers, digital ICs, and RF products group at STMicroelectronics, answering a query posed by EE Times at ST’s global press briefing.
The company has introduced a unified STM32 microcontroller platform spanning industrial, automotive, and robotics applications.
Maria Erez, executive VP of STMicroelectronics’ embedded processing subgroup, said the unified STM32 platform is designed to let customers reuse existing software, development expertise, and manufacturing investments across product generations and application domains.
View All The company also announced its STM32A automotive and high-safety MCU portfolio, a new STM32G5 mixed-signal MCU series, a high-performance STM32H7P with a neural processing unit (NPU), redesigned edge AI development tools, and STM32Cube for agentic AI.
El-Ouazzane said the semiconductor industry is experiencing convergence across automotive, industrial, and other domains in areas such as AI, security, safety, connectivity, sensing, and control, creating opportunities to reuse common technologies and expertise.
“Across many applications, the challenge for customers and developers is shifting from selecting a single device to finding ways to reuse software, tools, architectures, and engineering expertise across multiple products and evolving requirements,” El-Ouazzane said.
“In other words, the value is shifting from the individual MCU to the broader platform surrounding it.”
The company sees this convergence extending to systems that can perceive, make decisions, and act autonomously in the physical world, including industrial equipment, automotive systems, and robots. While these applications share requirements for connectivity, real-time control, safety, security, and software lifecycle management, their specific certification requirements may differ.
Arm partnership
As part of its longstanding partnership with Arm, STMicroelectronics said it will help define the future Cortex-M processor roadmap, aiming to set up a common foundation for embedded systems.
The partnership also gives ST early access to Arm cores, allowing it to optimize its platform for the processor roadmap.
Erez said STM32 has helped drive Cortex-M adoption. STMicroelectronics said more than 100 billion devices use Arm Cortex-M cores. STMicroelectronics is working with more than 100 ecosystem companies through Arm’s Total Design program for physical AI, announced in September.
Arm said the program brings ecosystem partners together to accelerate development in the market, with STMicroelectronics and Cadence as key partners.
“Now, we want the entire industry to adopt it,” a company spokesperson said during the Q&A session, referring to the Cortex-M roadmap.
“The next major frontier for computing is bringing intelligence into the machines that move, manufacture, and operate in the physical world,” said Drew Henry, executive VP, physical AI at Arm.
STM32A for automotive and high-safety systems
The STM32A family extends the STM32 platform into automotive and high-safety systems. The first three devices are sampling to customers, and STMicroelectronics plans broader availability in 2027.
The STM32A4 targets entry-level automotive local control nodes and safety systems. The STM32A5 addresses mid-range safety-critical applications in industrial automation, robotics, and energy systems, as well as automotive applications. The STM32A9 addresses safety-critical, multi-domain control where real-time control, low latency, Ethernet, and deterministic communication are essential.
“This is about continuity,” Erez said. “STM32A is not a separate story. It is an extension of the STM32 platform strategy, which we plan to continue and expand.”
Both STM32 and STM32A will be supported by the STM32Cube ecosystem. Process technologies and intellectual property (IP) such as Cortex-M cores, timers, clock sources, analog components, and power management can be reused across domains. Safety certification requirements still differ by market. Industrial and automotive products must meet different functional safety standards and qualification requirements, with automotive applications commonly assessed under ISO 26262 and its Automotive Safety Integrity Levels (ASILs).
The company said future STM32 products will be built on its 40-nm flash technology and 18-nm fully depleted silicon-on-insulator (FD-SOI) technology with embedded phase-change memory, supplemented by TSMC’s embedded non-volatile memory (eNVM) technologies.
Erez said nearly 2 million unique developers use the STM32 ecosystem, and the number of active users has grown by around 20% over the past three years.
The company said customers choosing its Stellar automotive MCUs or STM32A have different application requirements. ST also sees more automotive companies moving into humanoid robotics, citing commonalities in functional safety and architecture. The company expects this trend to continue in the coming quarters and years.
STM32G5 for digital power and motor control
The STM32G5 series of mixed-signal MCUs is in production now. It uses an Arm Cortex-M33 core running at 280 MHz. ST said the series delivers twice the compute performance of the STM32G4 series.
The company targets the series for data centers, robotics, photovoltaic installations, wind turbines, industrial drives, and commercial drones.
“This new generation of microcontrollers addresses the challenge to meet the growing energy needs of AI data centers efficiently, realize more sophisticated humanoid robots, and power more complex motor-control systems,” said Patrick Aidoune, general manager, general purpose and automotive microcontrollers division at STMicroelectronics.
ST said GPU clusters are driving rack power toward 1 MW, and the largest data centers could demand more than 2 GW of peak power by 2030. The company said the STM32G5 is the first MCU with the company’s enhanced 12-channel high-resolution timer. Equipment makers can use it to reconfigure power supply units dynamically as server workloads change. The timer also lets the MCU handle the higher switching frequencies of power supplies built with silicon carbide (SiC) and gallium nitride (GaN).
The company also said that robotics company Ruiyan Intelligent is using the STM32G5 in its next-generation robotic hands.
“In humanoid robotics, dexterity depends on combining strength, precision, sensing, and real-time control in an extremely compact space,” said Paul Tian, CEO of Ruiyan Intelligent.
The company said the series includes a programmable logic array, PLAy, which lets developers implement custom hardware functions on chip. Security features include Arm TrustZone, an immutable root of trust for secure boot and firmware updates, and support for ST’s post-quantum cryptography (PQC) libraries. The device targets Platform Security Architecture (PSA) Level 3 certification.
STM32G5 devices offer 64 Kbytes to 512 Kbytes of on-chip flash, in packages from QFN32 to LQFP128. Prices start at $1.034 for orders of 10,000 pieces. ST plans to add dual-core products to the series in 2027.
Edge AI hardware and tools
The STM32H7P combines a Cortex-M7 core with ST’s proprietary NPU for AI processing. It will enter volume production in early 2027. Aidoune said one alpha customer working on solar inverters moved from managing arc-fault circuit breakers across four panels to 20 panels using a single MCU.
ST began rolling out new and redesigned edge AI tools at the end of October. These include Model Zoo for finding models, Model Factory for customizing and optimizing models, Dataset Factory for organizing datasets, and tools for basic model testing on ST boards. ST said it offers more than 400 pre-validated models and has supported more than 260,000 active projects with its AI tools over the past 12 months.
ST said deploying AI models on MCUs depends far more on development tools than on computing power.
STM32Cube for agentic AI gives AI agents access to STM32 knowledge, resources, and toolkits through a model context protocol (MCP) server. It provides validated reference code and STM32 code generation and review tools.
Aidoune said the tool complements STM32 Sidekick, ST’s AI-based technical support tool. “STM32 Sidekick helps answer developers’ technical questions, while STM32Cube for agentic AI integrates into the coding workflow itself,” he said.
El-Ouazzane said ST needs to strengthen its MCU portfolio with single-core and multicore devices, different security levels, and different types of accelerators, peripherals, and memory. “Today’s announcement of STM32Cube for agentic AI is a step in the right direction, but there is more we can do,” he said.
Ecosystem over compute
Responding to a question from EE Times China on whether the industry is heading toward a computing power race, ST said its ecosystem will be its biggest differentiator. The company expects multicore architectures to become a trend within the cost constraints of the microcontroller category.
The company defined physical AI as a subset of edge AI. Physical AI systems perceive, decide, and act in the physical world, with actual movement through components such as joints and actuators. It said this is where the convergence between automotive and industrial applications is strongest.
“The industry needs less fragmentation and a stronger focus on helping MCU technologies converge around a common foundation, while maximizing software reuse,” El-Ouazzane said.
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