Is Hailo8 device meant for inference or training or both?

General Discussion
1

hi Hailo Team,

my question as described in the title.

2

I asked ChatGPT. It wrote a really nice answer. :slight_smile:

The Hailo-8 is designed for inference only, not for training.

:white_check_mark: What it’s meant for

  • It is an edge AI inference accelerator that runs already-trained neural networks efficiently on devices.
  • It’s optimized for:
    • Real-time predictions (e.g., object detection, segmentation)
    • Low power consumption (~2–3W)
    • High throughput (up to 26 TOPS)
  • Typical use cases: cameras, robotics, automotive systems, IoT devices.

:backhand_index_pointing_right: In short: deploying models, not creating them.

:cross_mark: What it’s NOT meant for

  • Training neural networks (backpropagation, gradient updates)
  • Large-scale model optimization loops

Training is usually done on:

  • GPUs
  • TPUs
  • High-end CPUs

:brain: How the workflow typically looks

  1. Train model on GPU/CPU (PyTorch, TensorFlow, etc.)
  2. Export model (e.g., ONNX)
  3. Compile/convert it using Hailo’s toolchain
  4. Deploy compiled model to Hailo-8 for inference

:balance_scale: Why this separation exists

Hailo-8’s architecture is:

  • Highly specialized for forward pass execution (inference)
  • Optimized for INT8 / efficient dataflow processing
  • Not designed for the heavy memory + compute patterns required for training

:white_check_mark: Bottom line:
Hailo-8 = inference accelerator only.
Training must be done elsewhere, then deployed onto it.