Hailo-8™ M.2 AI Acceleration Module

Step Up Your Edge Product Performance with Best-In-Class AI Processor Packaged in A Module

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The Hailo-8™ M.2 Module is an AI accelerator module for AI applications, compatible with NGFF M.2 form factor M, B+M and A+E keys. The AI module is based on the 26 tera-operations per second (TOPS) Hailo-8™ AI processor with high power efficiency. The M.2 AI accelerator features a full PCIe Gen-3.0 2-lane interface (4-lane in M-key module), delivering unprecedented AI performance for edge devices. The M.2 module can be plugged into an existing edge device with M.2 socket to execute in real-time and with low power deep neural network inferencing for a broad range of market segments. Leveraging Hailo’s comprehensive Dataflow Compiler and its support for standard AI frameworks, customers can easily port their Neural Network models to the Hailo-8 and introduce high-performance AI products to the market quickly.

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Hailo-8™ M.2 AI Acceleration Modules
AI Accelerator - Hailo-8 M.2 Module
  • Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8™ AI Processor
  • Best-in-class power efficiency
  • Enabling real-time, low latency, and high-efficiency AI inferencing on edge devices
  • Highest cost-efficiency (TOPS/$) compared with existing solutions
  • Scalable, enabling simultaneous processing of multi-streams & multi-models
  • Robust software suite supports state-of-the-art deep learning models & applications out-of-the-box
  • Supporting extended temperature range of -40°C to 85°C
  • Fast time to market using a standard form factor module, with key M, key B+M & key A+E
    → Optional extensions for Key M & Key B+M
Form Factor M.2 Key M M.2 Key B+M M.2 Key A+E
AI Processor Hailo-8™ AI processor with up to 26 TOPS and best-in-class power efficiency
Dimensions 22 x 42 mm
With breakable extensions to 22 x 60 and 22 x 80 mm		 22 x 30 mm
Interface PCIe Gen-3.0, 4 lanes (up to 32 Gbs) PCIe Gen-3.0, 2 lanes (up to 16 Gbs)
Supported Frameworks TensorFLow, TensorFlow Lite, ONNX, Keras, Pytorch
Supported OS Linux, Windows
Certification CE, FCC

Significantly better performance at the same cost and power consumption

  • Hailo-8 figures are based on SDK version 3.12.0 (November 2021), measured at room temperature on a single Hailo-8 device through PCIe interface on a Hailo evaluation board (system host: Intel® Core™ i5-9400 CPU @ 2.90GHz)
  • Intel Myriad X figures sourced from: https://docs.openvinotoolkit.org/latest/openvino_docs_performance_benchmarks_openvino.html , retrieved 12/07/21 for version 2021.4
  • Google Edge TPU figures sourced from: https://coral.ai/docs/module/datasheet/ (with power figures) ; https://coral.ai/docs/edgetpu/benchmarks/ (throughput only), retrieved 12/07/21 ; and https://coral.ai/models/image-classification/, retrieved 08/08/21
    • FPS is converted from latency in ms (1 divided by ms/1000)
  • Hailo-8 and Edge TPU figures are for batch=1 and INT8, while Myriad X is batch=1 and FP16

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Title File type Date modified
Hailo-8™ M.2 Extended Temperature Product Brief PDF 07/12/2022

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EU Research and Innovation Program

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This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 849921

This project has received funding from the European Union’s Horizon 2020.