Hailo-8™ High Performance PCIe Card ​

Prototype & Develop High-Performance, High-Efficiency Edge AI Video Analytics Systems with Multiple Hailo-8™ AI Processors​

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Today’s intelligent vision systems are high-resolution, high-frame-rate and multi-sensor, requiring more powerful and accurate real-time AI processing power.​

To support developers of high-performance edge video analytics systems across industry verticals, the Hailo industry-leading Hailo-8 AI processor is now available in a PCIe form-factor, delivering a range of 50 to 208 TOPS of AI performance at a low power consumption.

This standard form factor PCIe card, is supported by the robust Hailo software toolchain and a suite of dedicated AI developer tools and out-of-the-box applications.​

Learn about our partner ecosystem here

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Hailo-8™ Century Evaluation Platform
  • 4x Hailo-8 M.2 22X42 mm Acceleration Modules on a 16-lane PCIe Gen 3 adapter board​
  • Dimensions: 111mm x 210mm​
  • Standard PCIe form factor enters slot with no auxiliary power requirement​
  • Delivers up to 104 TOPS of AI performance at a typical power consumption of 25W​
  • Best-in-class PCI AI acceleration card – better performance than GPU, at lower power and lower cost​
  • Dedicated enablement S/W package and AI developer tools, with out-of-the-box support for state-of-the-art NN models​
  • Designed to enable development & prototyping of high-performance and efficiency real-time AI video analytics solutions, such as high-throughput Edge AI Boxes, dedicated video edge servers, industrial PCs and gateways

Hailo-8™ Multi-Device PCIe Card is also available as a reference design for our partners to design their own PCIe AI Accelerator cards with up to 6 Hailo-8™ AI Processors.


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  • Hailo-8™ Century Evaluation Platform performance figures are measured at room temperature for INT8
  • Nvidia T4 figures are for INT8, batch=8. Source: https://developer.nvidia.com/deep-learning-performance-training-inference

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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.