Imagine teaching a child what a cat looks like. You show them a Siamese, a Persian, a Maine Coon, a Domestic Shorthair, and a Turkish Van. A week later, you show them a new breed, a Scottish Fold, one they’ve never seen before. Without hesitation, the child says, “That’s a cat.”
This act of recognizing something new based on patterns they've already learned is a great analogy for AI inference.
Inference: The Execution Phase of AI
In machine learning, inference is what happens after training is complete. It’s when a model applies what it has learned to new, unseen data. In the cat example, the model has learned the features of "cat-ness", fur texture, ear shape, and facial structure, then uses that knowledge to evaluate new input.
Training happens on powerful servers with large datasets. Inference, on the other hand, is lightweight by comparison, it's about applying that training quickly, efficiently, and reliably.
This is where the Hailo-8 comes in.
Why Use the Hailo-8 for Inference?
The Hailo-8 is a purpose-built AI accelerator designed to run inference at the edge, meaning close to the source of the data, like a camera or sensor, rather than sending everything to the cloud. This brings three major advantages:
- Speed: Inference is performed in real time, ideal for applications like vision systems, autonomous machines, or real-time alerts.
- Efficiency: The Hailo-8 performs these computations using far less power than a GPU or CPU, with a tiny thermal footprint.
- Privacy: Data can be processed locally without needing to transmit everything to the cloud, which is valuable in regulated industries.
Inference for More Than Vision
While most examples of inference involve images, object detection, facial recognition, scene classification, the same principle applies to other sensor data:
- Audio: Detecting keywords or recognizing speaker identity.
- Environmental Sensors: Identifying gas signatures, temperature anomalies, or vibration patterns.
- Touch or Force Sensors: Interpreting physical gestures or applied pressure.
For example, a gas sensor detecting a chemical pattern might feed data into a trained model that recognizes specific leak signatures. The Hailo-8 could run this model locally and instantly trigger an alert without needing external processing.
Comparing Inference on CPU, GPU, and Hailo-8
| Feature | CPU | GPU | Hailo-8 AI Accelerator |
|---|---|---|---|
| Power Efficiency | Low | Medium | High |
| Optimized for AI | No | Yes | Yes (specifically) |
| Real-time Performance | Limited | Good (high-power) | Excellent (low-power) |
| Ideal Location | Cloud/Desktop | Data center/edge | Edge (e.g., in device) |
A Real-World Use Case: AI at the Display Edge
In our ALC-4096-AIH board, integrating a Hailo-8 with a Raspberry Pi CM5 and display controller, inference happens at the same place the data is generated and displayed. For example, a system might analyze camera input to detect people, count visitors, or spot hazards, all in real time on a display-integrated AI edge device.
This enables smarter devices in sectors like industrial automation, retail analytics, transportation, or security, without relying on cloud bandwidth or latency.
Conclusion
Inference is where AI meets the real world. It’s not just about models and training, it’s about recognizing, responding, and acting in real time.
The Hailo-8 makes that possible at the edge, whether it's identifying a cat, flagging a temperature spike, or triggering alerts from a live video feed, all in a tiny, power-efficient module designed for deployment in real devices.
AI at the display edge is no longer theoretical. It’s here.