Roofline AI provides an integrated software suite for compiling, executing, and measuring AI model performance on heterogeneous edge systems and system-on-chips (SoCs). Designed for edge AI engineers and hardware IP vendors, the platform bridges the gap between deep learning frameworks and resource-constrained edge silicon spanning Arm, Apple Silicon, Qualcomm, Nvidia, and NXP architectures.
At the core of the suite is an ahead-of-time (AOT) compiler SDK built upon MLIR and IREE architectures. The SDK ingests trained models from PyTorch, TensorFlow, TensorFlow Lite, and ONNX formats through a Python interface, applying target-specific graph optimizations, quantization pathways, and dynamic shape handling to generate compact, highly optimized device executables.
For on-device execution, Roofline AI delivers a lightweight, dependency-free C runtime capable of orchestrating multi-model pipelines across heterogeneous CPU, GPU, and NPU cores on Linux, macOS, Windows, and bare-metal platforms. A custom hardware abstraction layer (HAL) allows semiconductor vendors to expose proprietary NPU accelerators without rewriting top-level application logic.
The suite is completed by a unified Performance Dashboard providing command-line and visual telemetry for latency, token throughput, memory footprint, and model coverage across target devices. With support for edge language models, computer vision, and sensor fusion, Roofline AI accelerates edge AI deployment through reproducible compile-to-device workflows.
