Hailo-8 vs. Hailo-8L: Choosing the Right Edge AI Accelerator
Edge AI has evolved from a futuristic concept into a core requirement for modern smart devices, industrial automation, and single-board computers like the Raspberry Pi 5. Among the leading hardware options powering this revolution are Hailo’s specialized neural network processors.
If you are looking to accelerate computer vision and deep learning tasks at the edge, you will likely encounter Hailo’s two prominent siblings: the Hailo-8 and the Hailo-8L. While they share a foundational architecture, they cater to different performance tiers, power budgets, and project budgets.
At a Glance: Key Specifications
| Feature | Hailo-8 | Hailo-8L |
| :— | :— | :— |
| Peak Performance (INT8) | 26 TOPS | 13 TOPS |
| Compute Resources | Full architecture | Half the compute elements of the Hailo-8 |
| Typical Power Consumption | ~up to 8.25W (varies by form factor) | ~up to 6.6W (varies by form factor) |
| Form Factors | M.2 (Key M, B+M, A+E) | M.2 (Key B+M, A+E) |
| PCIe Interface | Up to Gen3 x4 (Key M) or x2 | Gen3 x2 |
| Primary Use Case | Demanding multi-stream or heavy vision tasks | Cost-sensitive, moderate edge vision & NVR |
Core Differences: Power vs. Performance
The fundamental architectural difference is straightforward: the Hailo-8L features roughly half the compute elements of the standard Hailo-8.
- Hailo-8 (26 TOPS): Operating at a robust 26 Tera Operations Per Second (TOPS) using INT8 arithmetic, the standard Hailo-8 is built for heavy-duty edge workloads. It handles complex, high-resolution video streams, large neural networks, and concurrent multi-model pipelines with ease.
- Hailo-8L (13 TOPS): Serving as the streamlined, cost-effective vision option, the Hailo-8L delivers 13 TOPS. Because it shares the same underlying architecture and runs at an equivalent core clock speed, it behaves like its older sibling—just with half the muscle.
Software, Compilation, and Ecosystem
A common question among developers is whether compiled model files (HEF files—Hailo Executable Format) are cross-compatible between the two chips.
The short answer is no, they are not directly interchangeable. Because the Hailo-8L has a different count of internal compute resources, you must compile or re-target your neural network models specifically for the intended hardware architecture using the Hailo Dataflow Compiler. However, the compilation workflow remains identical; you simply designate the target architecture flag during the final binary generation step.
Leave a Reply