Silicon Labs, the leading innovator in low-power wireless connectivity, announced a set of developer initiatives that makes it easier to build, extend, and operate increasingly capable IoT devices.
Unveiled at the company’s seventh annual Works With Summit, the new offerings expand what developers and their AI agents can create on the Silicon Labs platform, while helping customers develop, deploy, and manage intelligence at the edge.
Silicon Labs’ Simplicity AI SDK is now available in Public Beta with new features. The new Hardware Intent Agent, part of Simplicity Design Intelligence, helps developers create hardware configurations to begin app development. Silicon Labs is now a Databricks Partner, bringing embedded edge intelligence into the data and AI platform.
Design with AI: The Simplicity AI SDK is now in public Beta and supports AI coding assistants including GitHub Copilot, Cursor, and Codex. Silicon Labs is also introducing Simplicity Design Intelligence, a broad and growing set of capabilities designed to understand hardware and software intent, help customers turn that intent into a working product, and verify the implementation against the original intent. Hardware Intent is the first capability.
Build and extend: The Silicon Labs open-E), giving developers access to sample applications and tooling and a direct way to raise issues, propose fixes, contribute code, and extend the Silicon Labs software platform
Scale edge intelligence: Silicon Labs’ platform-agnostic edge AI and machine learning tools can now connect with Databricks, the data and AI company, so customers can manage data, models, embedded optimization, and hardware test results with governed enterprise data and AI workflows on the Databricks Data + AI platform.
Development Complexity Cannot Scale with IoT
As connected products add intelligence and custom hardware, building them grows more complex. Developers navigate specialized SDKs, configuration tools, debugging environments, and hardware constraints while using general-purpose AI assistants that do not inherently understand Silicon Labs devices or embedded workflows. Development teams also need better ways to share improvements and manage AI models as intelligence moves from the cloud onto edge devices. If workflows become harder with every new capability, development complexity becomes a barrier to scaling IoT.
Simplicity AI SDK and Simplicity Design Intelligence Connect Intent to Product
Simplicity AI SDK has entered public Beta, giving developers and their AI coding assistants structured access to Silicon Labs SDKs, tools, documentation, and connected hardware. Rather than requiring a proprietary AI assistant, the SDK works with tools developers may already use. GitHub Copilot, Cursor, and Codex are supported in the Beta. This grounding gives general-purpose AI assistants Silicon Labs-specific context. The first officially supported experience focuses on Bluetooth® Low Energy (LE), with workflows spanning project creation and configuration, building, flashing, debugging, network and power analysis, documentation search, and hardware interaction.
Simplicity Design Intelligence extends that foundation. It is a broad and growing set of Silicon Labs capabilities designed to understand hardware and software intent, help customers realize that intent in a working product, and verify the implementation against the original intent. Hardware Intent is the first capability. It uses product requirements, board schematics, and other input hardware documentation from datasheets to meeting notes to guide pin, peripheral, and software configuration, then compares the result with the original intent. This can find pin conflicts, peripheral mismatches, and missing constraints before fabrication and help reduce avoidable board respins.
Collaborate, Build, and Extend Through the Open-
Drawing on its experience advancing open-launching its open-e mantra is simple: build and extend. Developers can build with Silicon Labs sample applications and tooling, then extend the platform by raising issues, suggesting fixes, and contributing through pull requests
Accepted contributions can move through the company’s normal engineering and testing processes into future SDK releases. Even before acceptance, proposed fixes and discussions can remain visible to other developers, giving the community another way to share knowledge and solve common problems. The Silicon Labs applications team will support issues raised on GitHub, and Silicon Labs plans to expand this approach to additional wireless technologies over time.
Scale the Complete Edge AI Lifecycle with Databricks
As edge AI grows, model development also needs to connect with the data and AI infrastructure enterprises already use. Silicon Labs is partnering with Databricks to bring embedded edge AI into Databricks, creating a unified, governed foundation for connecting edge devices with enterprise data, AI, and machine learning workflows.
An initial Silicon Labs MLOps SDK experience connects devices with Databricks to help capture data from the device fleet. Once the data is in Databricks, all the familiar MLOps tools and training pipelines and GPU resources are available directly for training. Once a model is trained, the Silicon Labs ML Profiler can provide directionally accurate feedback on whether a model fits target hardware and its memory and CPU requirements. That feedback can help ML engineers iterate while remaining in a familiar Databricks environment, connecting model development with the enterprise data and governance that underpin production AI. This approach helps enterprises extend AI from the cloud to the edge without creating separate data and AI stacks for embedded workloads.
A Platform that Developers Can Build and Extend
Together, these initiatives scale the development system at three levels:
The Simplicity AI SDK and Simplicity Design Intelligence help developers and their AI agents move from hardware and software intent to a working product, with Hardware Intent as the first of a growing set of Design Intelligence capabilities.
Silicon Labs edge AI tools and Databricks connect embedded AI development with enterprise data and AI governance. Silicon Labs intends to make increasingly capable silicon easier to use and earn the position of platform of choice for embedded wireless development.
The Simplicity AI SDK Beta is publicly available, with official support initially focused on Bluetooth LE and support for GitHub Copilot and Codex. An alpha release of Hardware Intent, the first capability in Simplicity Design Intelligence, is planned for January 2027.
The Silicon Labs open-plications are accessible on GitHub for code review and contributions
Initial Silicon Labs MLOps tools are available in Databricks today.
Manish Kothari, Senior Vice President of Software at Silicon Labs
More capable silicon should not create more development complexity
Limor Alkelai, Co-CEO, at Risco Group
We’ve been working closely with Silicon Labs as an Alpha customer, evaluating how the Hardware Intent Agent can streamline hardware development across our current and future products while providing feedback on new features as it progresses towards general release. We see its potential in enabling quicker and more robust development.
- artificial intelligence
- silicon labs
- multi access edge computing
Ray Sharma is an Industry Analyst and Editor at The Fast Mode. He has over 15 years of experience in mobile broadband technologies and solutions, conducting research and analysis on various technology segments and producing articles and write-ups on the latest developments within the sector. He is also in charge of social media engagement and industry liaisons.
Follow him on LinkedIn or Facebook. He can be reached at ray.sharma@thefastmode.com
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