Roofline

Roofline is a software ecosystem specializing in the deployment of edge AI (artificial intelligence at the edge). Its primary function is to facilitate the implementation of AI models on end devices, optimizing hardware performance and streamlining the workflow for developers and product manufacturers. This platform enables users to leverage the full potential of Systems-on-Chip (SoC), removing technical barriers between algorithm design and physical execution on silicon.

Roofline’s core technology is based on a next-generation AI compiler built on MLIR (Multi-Level Intermediate Representations) and IREE. This approach uses layered abstraction to translate models from major frameworks into optimized intermediate representations. Thanks to its flexible architecture, the compiler generates efficient executables for various hardware backends, enabling heterogeneous execution across the entire system. This is particularly useful for squeezing the maximum power out of NPUs (neural processing units), easily adapting to emerging hardware architectures.

The toolkit is structured around three fundamental pillars for the product lifecycle:

• Deployment SDK: A development kit based on its advanced compiler that offers a scalable and flexible deployment solution.
• Runtime Inference Engine: A comprehensive SoC-level solution that ensures stable model execution across various devices.
• Performance Dashboard: A monitoring platform designed to evaluate and track real-world AI performance in the target environment.

Regarding compatibility, Roofline works natively with PyTorch, TensorFlow, TensorFlow Lite, and ONNX. The system is equipped to process hundreds of popular architectures, including Hugging Face models and proprietary models, without requiring manual modifications. The compiler supports most common layers, operators, and quantization techniques, ensuring that private models work securely and right out of the box.

This tool is aimed at both end-product vendors looking to accelerate their time-to-market and IP and hardware providers who need to enable AI on their chips. It is compatible with any relevant CPU and mobile GPU, providing a robust infrastructure for creating more stable and efficient smart products. As a solution born as a spin-off from RWTH Aachen University, it combines technical rigor with a practical approach to disruptive innovation in connected devices and mobility.