Hailo

What is Hailo

Hailo is a family of edge AI processors and vision processors purpose‑built to run deep learning and emerging generative AI workloads directly on devices. By combining high throughput with low power and minimal latency, Hailo enables real‑time perception, video analytics, and AI‑enhanced imaging without sending raw data to the cloud. Its accelerators and camera‑centric SoCs, paired with a mature SDK and model toolchain, help teams deploy neural network inference at scale—making on‑device AI more private, responsive, and cost‑effective for embedded products and smart machines.

Hailo Main Features

• Edge AI acceleration: Optimized for neural network inference, from classic computer vision to compact generative AI models on device.
• Low latency, low power: Real‑time responses and long duty cycles suitable for battery‑powered or fanless systems.
• Flexible hardware options: Accelerator modules (e.g., M.2/mini‑PCIe/PCIe) and integrated vision processors for camera designs.
• Complete software stack: SDK with compiler, runtime, profiler, and sample apps to streamline model deployment and tuning.
• Broad model support: Import via ONNX and popular frameworks; tools for quantization (PTQ/QAT) to preserve accuracy.
• Video and vision pipeline: Hardware blocks and software pipelines for multi‑stream analytics and video enhancement at the edge.
• Scalable performance: Run multiple models concurrently and schedule workloads across streams to maximize utilization.
• Privacy by design: On‑device processing reduces cloud dependency and exposure of sensitive video or sensor data.

Who Should Use Hailo

Hailo suits device makers and integrators building smart cameras, video analytics systems, robotics and AMRs, industrial automation, retail intelligence, smart city infrastructure, drones, and kiosks. It is also a fit for teams adding on‑device LLM or multimodal features—such as summarizing events or powering voice assistants—where low latency, privacy, and power efficiency matter.

How to Use Hailo

1. Select your hardware: choose an accelerator module or a vision processor SoC based on performance, power, and I/O needs.
2. Install the SDK: set up drivers, compiler, runtime, and development tools on your target Linux environment.
3. Prepare the model: export to ONNX (or supported formats), apply post‑training quantization or train with QAT if needed.
4. Compile and optimize: use the Hailo compiler to generate deployable binaries and profile performance/latency.
5. Integrate inference: call the runtime API (C/C++/Python) and connect video pipelines (e.g., GStreamer) for multi‑stream workloads.
6. Validate and tune: balance throughput and accuracy, schedule multiple models, and refine memory/batch settings.
7. Deploy at scale: package artifacts, enable remote updates, and monitor performance in the field.

Hailo Industry Use Cases

In video security, camera makers embed Hailo to run person/vehicle detection, tracking, and video enhancement entirely on device. Retail teams deploy shelf monitoring, queue analytics, and loss prevention with multi‑camera edge inference to reduce bandwidth and costs. Robotics manufacturers use Hailo for object detection, pose estimation, and scene understanding to improve navigation and safety. Smart city integrators run traffic analytics and incident detection at intersections. Kiosks and appliances add compact on‑device LLMs for private, low‑latency assistance.

Hailo Pricing

Hailo is offered as hardware accelerators and vision processors, with pricing varying by module, configuration, and volume. Evaluation and development kits are available through channel partners. The SDK and model tools are typically provided for development without per‑inference fees. For quotes, volume discounts, and reference designs, contact Hailo or authorized distributors.

Hailo Pros and Cons

Hailo FAQs

• Q1: Which model formats and frameworks are supported?

  Models are commonly imported via ONNX, with workflows originating from PyTorch or TensorFlow and exported for the Hailo compiler and runtime.

• Q2: Can Hailo run generative AI on the edge?

  Yes. It supports compact LLMs and multimodal or diffusion‑style workloads sized for edge deployment, with performance dependent on model footprint and memory.

• Q3: What quantization does Hailo support?

  Workflows include post‑training quantization and quantization‑aware training to retain accuracy while achieving high efficiency on the accelerator.

• Q4: How do I handle multiple camera streams?

  Use the runtime scheduler and pipeline tooling to allocate resources across streams, run multiple models concurrently, and tune batching for throughput.

• Q5: What operating environments are recommended?

  Hailo targets Linux‑based edge systems and offers C/C++/Python APIs, with reference integrations for common video pipelines.