Silicon Labs is expanding its developer platform with AI-assisted coding, open-source development and enterprise machine-learning tools as manufacturers look to add intelligence to increasingly constrained Edge devices.
The chipmaker announced the initiatives at its Works With Summit today, including a public beta of its Simplicity AI SDK, a new set of design-intelligence tools and a partnership with Databricks aimed at connecting embedded AI development with enterprise data and machine-learning workflows.
The Simplicity AI SDK gives AI coding assistants access to Silicon Labs’ software development kits, documentation, tools and connected hardware. The beta supports GitHub Copilot and Codex, with the initial officially supported workflows focused on Bluetooth Low Energy.
The company said the approach is intended to give general-purpose AI assistants more context about embedded development, allowing them to help with tasks including project creation and configuration, building, flashing, debugging, power and network analysis, documentation searches and hardware interaction.
Silicon Labs is also developing Simplicity Design Intelligence, a broader set of tools intended to translate hardware and software requirements into working implementations and check the resulting design against the original requirements.
Its first capability, Hardware Intent, is designed to work from information including board schematics, datasheets and product requirements. Silicon Labs said it can use this information to guide pin, peripheral and software configuration, while identifying conflicts and missing constraints before a board reaches fabrication.
The company expects an alpha release of Hardware Intent in January 2027.
Silicon Labs is also launching a beta open-source developer community, initially focused on Bluetooth LE. Developers will be able to submit issues, suggest fixes and contribute code through GitHub, with accepted changes potentially incorporated into future SDK releases following the company’s engineering and testing processes.
The initiative builds on Silicon Labs’ existing work with open-source technologies including Matter, Thread and Zephyr. The company said it intends to extend the community approach to other wireless technologies.
A separate initiative with Databricks is intended to connect Edge AI development with the data infrastructure already used by enterprises.
Silicon Labs’ initial MLOps tools allow data to be collected from connected device fleets and brought into Databricks, where existing training pipelines and GPU resources can be used to develop machine-learning models. Its ML Profiler can then assess whether a model is likely to fit the memory and processing constraints of target hardware.
The company said the approach could allow engineers to develop and optimise embedded models within existing enterprise data and governance environments, rather than maintaining separate AI workflows for Edge devices.
The announcements reflect a broader push by semiconductor and automation suppliers to use AI to reduce the complexity of developing increasingly sophisticated Edge devices. For industrial developers, the challenge is not simply generating code, but ensuring that software generated with AI remains consistent with hardware constraints and the requirements of the physical system.
“More capable silicon should not create more development complexity,” said Manish Kothari, Senior Vice-President of software at Silicon Labs. “We are giving developers and their AI agents a connected path from hardware and software intent through implementation, community contribution, and the complete AI lifecycle.”
The Simplicity AI SDK beta is available now, while the Silicon Labs open-source community beta launches with Bluetooth LE. Initial Silicon Labs MLOps tools are also available through Databricks.
