Hailo-8™ Century High Performance PCIe Cards
Prototype & Develop High-Performance, High-Efficiency Edge AI Video Analytics Systems with Multiple Hailo-8™ AI ProcessorsProduct Inquiry
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 accelerator is now available in a PCIe form factor, delivering a range of up to 208 TOPS of AI performance at a low power consumption.
The high-performance Century PCIe cards enable deep neural network inferencing in real-time with low power consumption for a broad range of market segments and applications on any platform with a 16-lane PCIe slot.
- Delivering 52-208 Tera Operations Per Second (TOPS)
- Supporting industrial temperature range of -40°C to 85°C
- Best-in-class power efficiency, at 400 FPS/W in ResNet50 benchmark model
- Highest cost-efficiency, starting at $249
- Enabling real-time, low latency, and highefficiency concurrent processing of complex pipelines with multi-streams and multi-models
- Robust software suite supports state-of-the-art deep learning models & applications out-of-the-box
|Small form factor||Wide host compatibility|
|# of TOPS||52||78||104||104||130||156||182||208|
|Form factor||single slot, HHHL||single slot, HHFL|
|Interface||PCleX16 slot with bifurcation||PCleX16 slot|
|Dimensions||69 X 168 mm||111 X 210 mm|
|Max thermal design power (TDP)||10W-25W||35-65W|
|Supported OS||Linux, Windows|
|Supported AI frameworks||Tensorflow, Tensorflow Lite, Keras, Pytorch & ONNX|
- 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
EU Research and Innovation Program
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.