The latest update to Analog Devices’ CodeFusion open-source development framework delivers a major step forward in simplifying work across multi-core and AI-driven systems. The enhanced platform now supports unified development and deployment across CPUs, GPUs, and edge environments allowing engineers to build and run AI models alongside traditional applications without leaving the open-source ecosystem.
Developers working with heterogeneous hardware have long faced fragmented workflows, juggling multiple tools for software development, model training, and deployment. This release addresses that gap by integrating AI model deployment directly into the system’s multi-core toolchain. The result is a streamlined environment where developers can create, optimize, and deploy applications and machine learning models from a single interface.
By combining open-source flexibility with cross-platform performance, the framework simplifies scaling from prototype to production. Its modular design supports a wide range of compute targets, from embedded devices to high-performance clusters, making it especially useful for developers tackling edge AI or distributed compute projects.
The update underscores the community’s commitment to accelerating innovation in open-source hardware and software design. It also strengthens the platform’s position as a bridge between traditional embedded development and modern AI deployment workflows.
To get started, developers can download the latest version from the project’s open-source repository, review the setup guide for configuring multi-core systems, and explore sample AI deployment workflows. The community forum and GitHub issues are open for collaboration, feedback, and future contributions.
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Filed Under: Applications, Embedded, Machine Learning, Products
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