FAQ from Hailo
What makes Hailo processors uniquely suited for real-time edge AI?
Hailo’s proprietary dataflow architecture eliminates bottlenecks common in von Neumann-based accelerators—enabling deterministic, sub-10ms inference latency, consistent frame rates for video pipelines, and guaranteed real-time scheduling—even under variable workload conditions. Combined with hardware-level time-slicing and QoS controls, Hailo ensures predictable AI execution where timing is non-negotiable.
How does Hailo balance raw AI performance with power efficiency?
Hailo achieves industry-leading TOPS per watt by co-designing hardware microarchitecture, memory hierarchy, and compiler optimizations. Its sparse computation engine skips redundant operations; its near-memory processing minimizes data movement; and its adaptive voltage/frequency scaling dynamically matches power consumption to workload demands—resulting in up to 4× better efficiency than competing edge AI chips.
Can Hailo processors run both classical computer vision and modern generative AI models?
Yes—Hailo supports a broad spectrum of AI workloads, from YOLO and ResNet-based perception models to quantized LLMs (e.g., Phi-3, TinyLlama), diffusion-based image generators, and multimodal transformers. The Hailo Software Suite includes specialized tooling for both vision (Model Explorer Vision) and generative AI (Model Explorer GenAI), with seamless integration into PyTorch and ONNX ecosystems.
What support does Hailo provide for developers transitioning to edge AI deployment?
Hailo offers comprehensive developer enablement: free cloud-based evaluation environments, detailed reference designs (including carrier boards and camera stacks), production-grade BSPs and drivers, certified partner integrations (e.g., NVIDIA JetPack, Qualcomm QCS), and direct engineering support through the Hailo Partner Program—accelerating time-to-deployment from months to weeks.