AI Inside Embedded Development Webinar

Embedded software teams want to bring generative AI into how they build software, but standard cloud AI services rarely fit: source code, design documents, and customer IP often can't leave the company network. At the same time, AI models that hit accuracy targets in the cloud frequently miss latency, power, or memory budgets once they run on the target SoC—the model works, but the hardware doesn't. In this 40-minute webinar, we walk through how embedded teams are addressing the first of those problems—bringing AI into the development process in a way that fits their constraints—and where performance engineering on the target hardware fits in the broader picture. Drawing on engagements with automotive and industrial customers including Toyota and Renesas, the session covers in-house LLM environments, real use cases from embedded development workflows, and how teams decide where to invest first. This is a focused look at how AI actually fits into embedded development today, and the engineering approaches that make it work in practice.