Add AI inference to an embedded host
The Hailo-8 M.2 AI Acceleration Module is designed to add dedicated neural network processing to an existing edge computing platform. Part number HM218B1C2FAE is the 2280 M Key extended-temperature version. It uses the Hailo-8 processor, which delivers up to 26 TOPS of AI compute for on-device inference.
Processing AI workloads at the edge allows a host system to analyze camera or sensor data where it is generated. This makes the module a practical choice for applications that need local computer vision, such as identifying objects in video, classifying images or inspecting products on a production line. The module is an AI accelerator for a host computer; it does not replace the host processor, camera interface or the rest of the system.
Multi-stream video analytics and machine vision
The Hailo-8 architecture supports multiple models and streams, enabling developers to build pipelines with more than one AI task. For example, an application might use one model to detect objects and another to classify them. A video analytics system may process feeds from multiple cameras, subject to the resolution, models and throughput required by the deployment.
Potential uses include smart surveillance, industrial visual inspection, retail analytics, robotics and intelligent transportation systems. Performance in a finished product depends on the compiled models, input data, host platform and overall system design; the 26 TOPS figure describes the processor’s maximum AI compute capability, not a guaranteed frame rate for every application.
M.2 M Key integration
This variant uses the M.2 2280 format, measuring 22 × 80 mm, and connects through PCIe Gen 3 x4. The four-lane interface distinguishes it from the Hailo-8 B+M Key module, which uses PCIe Gen 3 x2. Selecting between the two variants should start with the host’s available slot and electrical specification.
Before ordering for an embedded PC, single-board computer or industrial gateway, confirm that the M.2 slot carries PCIe, supports the required lanes and has room for the 2280 board. The system also needs appropriate power delivery, cooling and software support. An M Key connector’s physical shape alone does not confirm that every host is suitable.
Software for model deployment
Hailo’s software ecosystem provides tools for moving trained neural networks into an edge application. The Hailo Dataflow Compiler prepares supported models for the accelerator, while HailoRT provides the host runtime used during inference. Hailo also provides model resources and application examples to help developers evaluate computer vision workloads.
Supported framework workflows include TensorFlow, TensorFlow Lite, Keras, PyTorch and ONNX. Hailo lists x86 and Arm host architectures and Linux and Windows operating systems for the M.2 module family. Developers should check the current Hailo software release and host requirements when planning an integration.
Industrial deployment considerations
The HM218B1C2FAE module is the extended-temperature variant, specified from −40°C to 85°C. This makes it relevant to industrial edge equipment operating across a wider temperature range. The complete system must still be designed for its intended environment, including enclosure protection and thermal management.
Typical application areas: multi-camera video analytics, smart security systems, object detection, manufacturing inspection, robotic vision and AI-enabled edge gateways
