Hailo

Hailo is an edge AI technology company that designs specialized processors and software for running advanced artificial intelligence directly on edge devices. By moving neural network inference out of the cloud and onto local hardware, Hailo dramatically reduces latency, boosts energy efficiency, and keeps sensitive data private. Its solutions target industries ranging from automotive and industrial automation to retail and healthcare, enabling real‑time AI even in power‑constrained environments.

• Edge AI Processing: Hailo processors are built from the ground up to execute complex neural networks at the edge, delivering high throughput and low power consumption.
• Generative AI Accelerators: The Hailo‑10H M.2 module supports a wide range of transformer models and large language models (LLMs), making on‑device generative AI tasks practical.
• AI Vision Processors: The Hailo‑15 AI Vision Processor integrates advanced computer vision engines, pushing the boundaries of video analytics and intelligent camera applications.
• Comprehensive Software Suite: Includes the Hailo Dataflow Compiler, HailoRT runtime, a Model Zoo of pre‑optimized models, and the TAPPAS vision software package—simplifying deployment from training to production.
• Real‑Time Data Processing: By eliminating cloud round‑trips, Hailo enables instant, local decision‑making critical for autonomous systems and live video streams.
• Energy‑Efficient Architecture: Designed for mobile and embedded devices, Hailo chips minimize power draw without sacrificing performance.
• Scalable Solutions: The product line spans from low‑power modules for IoT devices to high‑end accelerators for demanding industrial workloads.

1. Select the appropriate hardware: Choose from Hailo‑8, Hailo‑15, or Hailo‑10H modules (M.2, mPCIe, or standalone vision processors) based on your performance and form‑factor needs.
2. Integrate the hardware into your device: Install the Hailo accelerator in an available M.2 slot, mPCIe slot, or embed it on a custom carrier board following Hailo’s design guidelines.
3. Install the software stack: Download and set up the HailoRT runtime, Dataflow Compiler, and any required drivers on your Linux‑based edge system (Windows support may be available for certain modules).
4. Choose or prepare an AI model: Use the Hailo Model Zoo for ready‑made, optimized models, or convert your own trained model from TensorFlow, PyTorch, or ONNX using the Dataflow Compiler.
5. Deploy and run inference: Load the compiled model onto the Hailo processor via HailoRT and integrate it into your application code. For vision tasks, TAPPAS provides ready‑to‑use pipelines.
6. Monitor and optimize: Analyze performance metrics using Hailo’s profiling tools, and fine‑tune model partitioning or precision settings to balance speed, accuracy, and power usage.

Hailo’s pricing is customized based on the hardware module, volume, and level of support required. Entry‑level development kits and low‑volume modules typically offer a cost‑effective path for startups and small enterprises. Industrial‑grade solutions for high‑demand environments are priced according to specifications, warranty, and dedicated engineering support. For accurate quotes and the latest pricing, refer directly to the official Hailo website.

• Edge hardware modules: Hailo‑8, Hailo‑15, and Hailo‑10H accelerators in M.2, mPCIe, and other form factors for direct integration into custom boards or existing systems.
• Software development kit (SDK): HailoRT runtime, Dataflow Compiler, and a rich set of APIs for C/C++ and Python enable flexible application development.
• AI model optimization tools: Command‑line utilities and graphical interfaces to convert, quantize, and compile models from major frameworks like TensorFlow, PyTorch, and ONNX.
• Pre‑built application pipelines: TAPPAS provides reference pipelines for common vision tasks, running out of the box on Ubuntu-based systems.
• Developer resources: Comprehensive documentation, tutorials, and a community portal to support integration across various Linux distributions and hardware platforms.

• Start with the Model Zoo: Pre‑optimized models save time and guarantee efficient hardware utilization; adapt them to your use case before training custom models from scratch.
• Leverage TAPPAS for vision applications: This software package offers building blocks for video decoding, analytics, and display, accelerating your product development cycle.
• Use the Dataflow Compiler iteratively: Fine‑tune quantization and partitioning settings to maximize throughput while maintaining acceptable accuracy for your specific workload.
• Test on actual hardware early: Simulated environments don’t expose real‑world bottlenecks; validate performance with a development kit as soon as possible.
• Engage with Hailo’s developer community: Forums, tutorials, and dedicated training sessions provide practical advice and troubleshooting help, especially with advanced integration scenarios.
• Plan thermal and power management: Even though Hailo processors are energy efficient, embedding them in sealed or fan‑less enclosures requires adequate heat dissipation.

• Automotive engineers working on ADAS, autonomous driving, or in‑cabin monitoring systems that demand real‑time, reliable AI.
• Security system integrators building intelligent cameras, access control panels, or perimeter protection solutions with on‑device analytics.
• Industrial automation professionals deploying visual inspection, predictive maintenance, or robotic guidance on factory floors.
• Retail technology providers creating smart checkout, shelf‑monitoring, or customer engagement platforms that need low‑latency, private processing.
• AI researchers and academics requiring a compact, high‑performance platform for edge AI experimentation and teaching.
• Healthcare equipment manufacturers designing portable or stationary diagnostic devices that must keep data local for privacy compliance.