Roofline

Roofline is a deployment toolkit for edge AI. It gives hardware vendors and product teams an SDK, a runtime, and a performance dashboard to get AI models running on chips like CPUs, GPUs, and NPUs. Instead of spending months hand-tuning models for each new piece of hardware, developers can use Roofline's compiler and inference engine to deploy models in a fraction of the time. The company calls it a "full SoC deployment toolkit" — and that's exactly what it is.

The core of Roofline is its AI compiler, which takes models from frameworks like PyTorch or TensorFlow and optimizes them for the target chip. The runtime then handles inference across different processors — CPU, GPU, NPU — without the developer having to write separate code for each. The performance dashboard lets teams track how models actually behave in the real world, not just in benchmarks. Roofline supports a wide range of models: LLMs (like Llama, Gemma, Qwen), vision models (YOLO, ResNet, MobileNet), language models, audio models, and radar models. The website lists dozens of pre-verified models, from tiny 230M-parameter LLMs to 8B-parameter ones.

This tool is built for hardware vendors who want to make their chips AI-ready, and for product teams who need to ship AI features on embedded devices. If you're building a smart camera, an industrial sensor, or any edge device that runs AI, Roofline saves you from writing low-level optimization code. It's a practical, no-nonsense solution for a problem that's usually painful: getting AI to run well on small, power-constrained hardware.