Silicon Labs Adds IoT Developer Platform Tools

Silicon Labs announced on Oct. 7 at its Works With 2026 Summit the public beta of the Simplicity AI SDK, which gives developers organized access to its documentation, tools and code libraries for AI coding assistants including GitHub Copilot and OpenAI's Codex, with the first release targeting Bluetooth Low Energy projects. The Austin, Texas-based company also introduced Simplicity Design Intelligence, whose first feature Hardware Intent converts natural-language hardware specs and board diagrams into agent-ready output, with an alpha version planned for January 2027, and opened a public beta of its open-source developer community starting with its BLE software stack on GitHub. Silicon Labs product manager Tamas Daranyi said the SDK is "not the solution, it's not magic, but it's a tool and very serious enablement," stressing that human oversight remains essential.

Silicon Labs today Oct. 7 announced initiatives to make it easier for developers to build connected IoT hardware and use edge AI. At its annual Works With 2026 Summit https://www.silabs.com/about-us/events/works-with-2026 , the Austin, Texas-based company introduced software development tools, an open-source software project, and a cloud integration partnership. These are meant to help engineers write code, check hardware designs, and deploy machine learning models on low-power microcontrollers. These announcements come as chipmakers face pressure from enterprise customers to make embedded software development easier. Limited memory, computing power, and radio options often slow down product launches. Rather than improving only silicon performance, Silicon Labs said it is working to address software development challenges by linking developer tools with generative AI assistants and enterprise data platforms. AI tools for embedded software A key part of the update is the public beta release of the Simplicity AI SDK https://www.silabs.com/software-and-tools/simplicity-ai-sdk . Silicon Labs said this software kit gives developers organized access to its documentation, tools, and code libraries for use with AI coding assistants such as GitHub Copilot and OpenAI’s Codex. The first release targets Bluetooth Low Energy projects, letting AI agents help with setup, building, flashing, debugging, and network analysis. Last week, ahead of the formal announcement, Tamas Daranyi, product manager at Silicon Labs, described the tool as something that boosts efficiency, not as a replacement for careful engineering. “It’s not the solution, it’s not magic, but it’s a tool and very serious enablement,” Daranyi told EE Times at the Pretzl Connect press event in Budapest, noting that developers can use the SDK to accelerate product customization and proof-of-concept testing. Daranyi addressed worries about code quality when using generative AI, stressing that human oversight is still essential during development. “You are still driving the car,” he told us. “Now it’s much faster, but it’s still a car, and you are the one driving it.” He also explained that engineers might not have to read every line of AI-generated code, but they still need to test the code thoroughly to make sure it works on the hardware. Bridging hardware intent and code Alongside the SDK, Silicon Labs introduced Simplicity Design Intelligence https://www.silabs.com/software-and-tools/simplicity-ai-sdk?tab=overview . This software framework helps turn high-level product ideas into working embedded setups. Its first feature, called Hardware Intent, looks at hardware specs and board diagrams to help choose pins and peripherals before building the actual boards. At the Budapest briefing, Sam Ponedal, head of PR at Silicon Labs, said that hardware design requirements often come in many different formats, including technical PDFs, handwritten notes, or video walkthroughs. Hardware Intent takes these natural-language inputs and turns them into a format that software agents can use. “Hardware Intent can take all of those inputs, natural language, no matter what format, and it can turn them into a flattened, agent-ready output that developers can immediately start using an agent to build their application,” Ponedal said. He explained that the system can automatically spot subtle problems, such as mismatched pin counts or missing peripheral assignments, between the original project requirements and the board diagrams. This helps avoid expensive hardware redesigns. Silicon Labs plans to release an alpha version of Hardware Intent in January 2027. Opening up BLE software Silicon Labs said it is also starting a public beta for its open-source developer community, beginning with its Bluetooth Low Energy BLE software stack. Developers can access sample applications and tools on GitHub, submit issues, suggest code fixes, and contribute pull requests. When developers suggest fixes, Silicon Labs’ engineering team will check them both automatically and manually. “If it meets those testing needs, we’ll implement it into our SDK, so it becomes an official part of the Simplicity SDK release,” Ponedal told us in Budapest. Daranyi explained that the open-source project covers application-level software and development tools, but not the lower-level radio protocols. “It’s very important that we don’t open source everything in the protocol,” Daranyi said. Proprietary radio layers and lower-level protocol details will stay closed, while higher-level sample applications will be open to encourage community collaboration. Enterprise MLOps at the edge To help run AI directly on microcontrollers, Silicon Labs has partnered with Databricks https://www.databricks.com/ , a data management company. This partnership connects edge devices to the Databricks Data + AI platform, so engineers can handle data collection, model training, and embedded optimization as part of their regular enterprise workflows. The first Silicon Labs MLOps SDK https://docs.silabs.com/machine-learning/2.2.2/sisdk-mc-learning-release-notes/ lets connected edge devices send telemetry data to Databricks for model training. After training, Silicon Labs said its conversion tools can convert models from PyTorch or ONNX into TensorFlow Lite. A Python-based ML Profiler inside Databricks checks how models will run on the target hardware. It gives information such as cycle counts, memory use, and energy consumption before deployment. This integration solves a common edge AI problem: Data science teams usually work in the cloud, while embedded engineers use specialized microcontrollers. By adding profiling and optimization tools directly to Databricks, developers can improve edge models without needing a separate setup. See also: Ambient IoT: From Battery-Free Promise to Mass-Market Reality https://www.eetimes.com/ambient-iot-from-battery-free-promise-to-mass-market-reality/ Smarter Buildings, Safer Occupants: Intelligence Meets Privacy https://www.eetimes.com/smarter-buildings-safer-occupants-intelligence-meets-privacy/