Build Edge AI Applications on Arm

An NPU (Neural Processing Unit) is a specialized hardware accelerator designed to efficiently run neural network inference by handling matrix and tensor operations, offloading this compute-intensive work from the CPU. In Arm-based systems such as those using Ethos-U NPUs, it enables faster and lower power on-device AI processing at the edge.

Yes, Arm provides several ways to test Edge AI applications without physical hardware. You can use the Arm Fixed Virtual Platform (FVP) to simulate Cortex-M and Ethos-U NPU behavior.

Arm's extensive ecosystem offers multiple development boards for Edge AI. Popular options include the Alif Ensemble E8 Devkit, (Cortex A-32, Cortex-M55, and Ethos-U85 and Ethos-U55 NPUs), Grove Vision AI V2, OpenMV, and Raspberry Pi 5.

Arm platforms support models from PyTorch (via ExecuTorch), LiteRT (formerly TensorFlow Lite), ONNX, and PaddlePaddle, with expanding compatibility to other machine learning frameworks as ecosystem support grows.

Cortex-M processors are optimized for ultra-low-power AI tasks such as sensor processing and always-on inference, typically running on an RTOS. They are designed to extract maximum performance within constrained memory and compute resources, especially when paired with Ethos-U NPUs for efficient neural network acceleration. In contrast, Cortex-A processors provide higher performance and larger memory capacity, making them better suited for more complex AI workloads like vision or speech processing under Linux.

Many existing ML models can run on Arm devices, as Arm platforms are compatible with major frameworks like PyTorch (via ExecuTorch), LiteRT (formerly TensorFlow Lite), ONNX, and PaddlePaddle. However, deployment depends on factors such as model size, memory footprint, supported operators, and available compute resources, which may require optimization or quantization for efficient execution on embedded and edge devices.