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The Hailo-8L is an AI accelerator IC designed for on-device inference in embedded systems. Part number HNC1LBI11BH delivers up to 13 TOPS of AI performance with 1.5 W typical processor power consumption. Its integrated memory eliminates the need for external DRAM, while Hailo’s software suite supports real-time computer vision, multiple AI models and a path to higher-capacity Hailo-8 designs.
The Hailo-8L Entry-Level AI Accelerator brings neural network inference to compact, power-conscious edge devices. Part number HNC1LBI11BH is the Hailo-8L processor IC for designs that need up to 13 TOPS of AI compute. Hailo specifies 1.5 W typical processor power consumption and an industrial operating temperature range of −40°C to 85°C.
Designed for embedded computer vision, the Hailo-8L can run AI tasks close to the camera or sensor. Applications include object detection, image classification, video analytics and visual inspection. Its architecture supports concurrent models and multiple video streams, allowing developers to build AI pipelines around their application’s performance and power requirements.
The processor has integrated memory and requires no external DRAM. This can simplify a chip-on-board design and reduce the components needed around the accelerator. It is an AI accelerator IC; customers looking for a ready-to-install M.2 card should use the separate Hailo-8L M.2 module listing.
Hailo’s software suite includes a dataflow compiler for preparing neural network models for deployment. It supports workflows using TensorFlow, TensorFlow Lite, Keras, PyTorch and ONNX, with x86 and Arm host architectures and Linux or Windows environments. Developers can also use the Hailo-8 software ecosystem when planning a move to a higher-capacity Hailo-8 design.
| Specification | Hailo-8L |
|---|---|
| Product type | Edge AI accelerator processor IC |
| Part number | HNC1LBI11BH |
| AI performance | Up to 13 TOPS |
| Typical processor power consumption | 1.5 W |
| External DRAM | Not required |
| Processing | Multiple streams and models |
| Industrial operating temperature | −40°C to 85°C |
| Supported AI frameworks | TensorFlow, TensorFlow Lite, Keras, PyTorch, ONNX |
| Supported host architectures | x86, Arm |
| Supported operating systems | Linux, Windows |
| Model deployment | Hailo Dataflow Compiler and software suite |
| Neural network | Input resolution | FPS | Measured power | FPS/W |
|---|---|---|---|---|
| ResNet-50 v1 | 224 × 224 | 504 | 1.9 W | 260 |
| ViT_base | 224 × 224 | 122 | 2.5 W | 48 |
| YOLOv5m | 640 × 640 | 106 | 2.9 W | 37 |
| YOLOv8m | 640 × 640 | 87 | 3.1 W | 28 |
| YOLOv11m | 640 × 640 | 58 | 2.3 W | 26 |
| stdc1 | 1,024 × 1,920 | 20 | 1.7 W | 12 |
Benchmark conditions supplied for the current listing: batch size 8; room temperature; PCIe connection to a Hailo-8L evaluation board; Intel Core i5-9400 host at 2.90 GHz; models compiled with Hailo Dataflow Compiler 3.31.0. Measured workload power should not be presented as the 1.5 W typical power figure.