Hailo Accelerator

A Hailo Accelerator is a dedicated chip for running AI models directly inside an edge device, where power, heat, and response time matter as much as accuracy. Think of it as a compact, highly specialized engine: instead of being flexible enough for every kind of computing task, it is built to move neural-network data efficiently and perform inference quickly.

What it does

Hailo accelerators, such as the Hailo-8 and lower-power Hailo-8L, execute trained networks for tasks including object detection, image classification, pose estimation, and segmentation. They are used alongside a host processor rather than replacing the whole computer. A camera system, for example, captures frames; the host prepares the image; the Hailo chip runs the neural network; and the host uses the results to trigger an alarm, record video, or control equipment.

How a model reaches the chip

A model trained in frameworks such as PyTorch or TensorFlow is converted and compiled with Hailo’s tools, notably the Hailo Dataflow Compiler. This produces a hardware-specific executable representation, commonly called an HEF file. At deployment time, the HailoRT runtime sends data to the accelerator and retrieves predictions.

The compiler maps network layers and data movement onto the chip’s architecture.
Quantization, commonly to INT8, reduces memory traffic and power use.
The runtime integrates inference into applications on Linux-based embedded systems.

Why it matters on devices

Continuous AI inference can overwhelm a CPU or drain a battery, while a GPU can add heat and require active cooling. A Hailo accelerator is designed to deliver high inference throughput per watt, making it useful in smart cameras, retail analytics, robotics, and industrial inspection systems that must work offline. It can appear on an M.2, mini-PCIe, or PCIe module connected to a system such as a Raspberry Pi-class board or an embedded x86/ARM computer. The trade-off is important: deployment requires using supported model operations and Hailo’s compilation flow, but the reward is fast, local AI within a fixed thermal and power budget.

Hailo Accelerator is a family of dedicated AI inference processors from Hailo, designed to run neural networks efficiently in embedded devices such as cameras, robots, and industrial systems. Its architecture delivers high inference throughput with low power consumption and latency. It enables real-time vision and sensor AI locally, avoiding the bandwidth, delay, privacy, and connectivity dependence of cloud inference.

Think of a Hailo Accelerator as a specialist co-worker inside a smart device. While the device’s main processor handles everyday tasks, the Hailo chip focuses on one demanding job: running AI quickly and efficiently.

For example, a security camera can use one to spot people, cars, or unusual activity right where the camera is installed. It does not need to send every video frame to a distant cloud server and wait for an answer.

This makes devices faster, more private, and less dependent on a reliable internet connection. Hailo accelerators are especially useful in cameras, robots, vehicles, and other products that need AI awareness without using lots of power.