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Hailo-8 M.2 AI Acceleration Module - HM218B1C2ZAE

Part No: HM218B1C2ZAE
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The Hailo-8 M.2 AI Acceleration Module (HM218B1C2ZAE) delivers up to 26 TOPS of on-device AI inference performance in an M.2 2280 B+M Key form factor. Using a PCIe Gen 3 x2 interface with typical power consumption of approximately 2.5 W, this extended-temperature AI accelerator is designed for real-time computer vision, video analytics, machine vision, robotics and industrial edge AI applications..

Datasheet
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  • Specification

The Hailo-8 M.2 AI Acceleration Module HM218B1C2ZAE is a high-performance edge AI accelerator starter kit designed to add dedicated neural-network inference capability to compatible embedded computers, industrial PCs, edge gateways and other systems with an M.2 interface.

 

Powered by the Hailo-8 AI processor, the module delivers up to 26 TOPS of AI inference performance while maintaining low typical power consumption of approximately 2.5 W. This combination allows demanding deep-learning workloads to run directly on edge devices, reducing reliance on cloud infrastructure and enabling faster local decision-making.

 

The HM218B1C2ZAE uses the standard M.2 2280 B+M Key form factor, making it suitable for integration into a wide range of edge computing platforms. Its PCIe Gen 3 x2 interface provides high-speed communication between the Hailo-8 accelerator and the host processor.

 

The module is designed for real-time computer vision and AI inference workloads such as object detection, image classification, intelligent video analytics, industrial visual inspection and multi-camera processing. It supports simultaneous multi-stream and multi-model inference, allowing compatible edge systems to process several video streams or neural networks concurrently.

 

Hailo-8 supports both x86 and ARM-based host architectures, giving developers flexibility when integrating the module into embedded computers, industrial systems and edge AI platforms. Supported operating systems include Linux and Windows.

 

The Hailo software ecosystem supports widely used deep-learning frameworks including TensorFlow, TensorFlow Lite, Keras, PyTorch and ONNX. The software suite includes HailoRT, the Hailo Dataflow Compiler and Hailo Model Zoo resources for optimisation, deployment and runtime execution of trained AI models.

 

The HM218B1C2ZAE is specified for an extended operating temperature range from -40°C to +85°C, making it suitable for industrial automation, transportation, outdoor systems and other demanding edge environments.

 

As a starter-kit configuration, the product includes thermal-management accessories to assist with evaluation and integration. This makes the HM218B1C2ZAE suitable for developers and system integrators building compact edge AI, machine vision and intelligent video analytics solutions.

  • Up to 26 TOPS AI inference performance

  • Typical power consumption of approximately 2.5 W

  • Manufacturer Part Number: HM218B1C2ZAE

  • M.2 2280 B+M Key form factor

  • PCIe Gen 3 x2 host interface

  • Powered by the Hailo-8 AI processor

  • Designed for real-time edge AI inference

  • Supports simultaneous multi-stream processing

  • Supports multi-model neural-network inference

  • Compatible with x86 and ARM host processors

  • Supports Linux and Windows

  • Compatible with TensorFlow, TensorFlow Lite, Keras, PyTorch and ONNX

  • Extended operating temperature from -40°C to +85°C

  • Suitable for industrial and embedded edge systems

  • Supports machine vision and intelligent video analytics

  • Suitable for smart cameras, robotics and industrial automation

  • Compact M.2 design for space-constrained systems

  • Starter kit includes thermal-management accessories

Specification Details
Manufacturer Hailo Technologies
MPN HM218B1C2ZAE
Processor Hailo-8
AI Performance Up to 26 TOPS
Typical Power Consumption Approx. 2.5 W
Module Type M.2 AI Acceleration Module / Starter Kit
Form Factor M.2 2280
Key B+M
Host Interface PCIe Gen 3 x2
Host Architecture Support x86, ARM
Operating Systems Linux, Windows
AI Frameworks TensorFlow, TensorFlow Lite, Keras, PyTorch, ONNX
Operating Temperature Min. -40°C
Operating Temperature Max. +85°C
Multi-Stream Processing Supported
Multi-Model Processing Supported
Primary Workload Deep Learning / Neural Network Inference
Deployment Type Edge / Embedded AI