Introduction: What is Edge AI and Why It Matters
In recent years, edge AI, which means running artificial intelligence tasks directly on devices such as cameras, sensors, small computers, or robots rather than in remote cloud servers, has grown rapidly. This shift is driven by demand for low latency, privacy, reliability, and energy efficiency.
Traditional AI setups often rely on powerful GPUs. GPUs are great for training large deep learning models in data centres or clouds. But when you want to run AI on-device, for example in surveillance cameras, drones, industrial systems, smart retail, or IoT devices, GPUs may not always be ideal. They may draw too much power, be bulky, or be overkill for simple inference tasks.
That is where specialized edge AI accelerators come in. One such solution is Hailo, designed from the ground up for edge AI. In many cases, Hailo accelerators outperform traditional GPUs for edge AI applications. In this article, we explore why.
What is Hailo: A Quick Overview
Hailo builds AI chips and modules specifically for edge devices. Their main offering, Hailo‑8 AI Accelerator, is a compact, energy-efficient AI processor that delivers up to 26 TOPS (Tera Operations Per Second) of compute performance.
Hailo‑8 and its entry-level sibling Hailo‑8L come in various form factors such as M.2 modules or PCIe cards which lets developers integrate them into different devices easily like embedded PCs, NVRs, IoT gateways, and small form factor computers.
Despite their small size, Hailo‑8 accelerators are powerful enough to run real-time deep learning inference tasks including vision models, object detection, video analytics, and more.
With this background, it makes sense to compare Hailo‑based edge AI and traditional GPU-based approaches for real-world edge deployment.
Limitations of Traditional GPUs for Edge AI
Before we highlight the advantages of Hailo, it helps to understand what makes GPUs less suitable for many edge AI use cases:
High power consumption. Typical discrete GPUs consume tens to hundreds of watts. In a battery-powered device, embedded system, or always-on IoT gadget this is often unacceptable.
Large size and need for cooling hardware. GPUs are bulky, generate heat, and often need active cooling. This complicates integration in small devices or uncontrolled environments.
Cost inefficiency for inference-only tasks. For simpler tasks like object detection, video analytics, or sensor data processing, a high-end GPU may be overpowered. This means paying for compute or memory capacity that the use case may not need.
Latency and reliability concerns. In real-time applications such as surveillance, video analytics, or industrial automation, the overhead associated with GPUs in terms of boot time, OS dependencies, and power draw may introduce latency or instability.
Not practical for mass deployment. If you need to deploy hundreds or thousands of devices such as smart cameras, retail sensors, or IoT endpoints, GPU-based setups become costly, power hungry, and bulky making large-scale deployment impractical.
Because of these limitations, many modern edge AI needs cannot be realistically or economically met using traditional GPUs.
Why Hailo AI Accelerators Often Outperform GPUs for Edge AI
Power Efficiency: Much Lower Power Draw and Smaller Size
Hailo‑8 delivers up to 26 TOPS while consuming only about 2.5 W of power. Compared to typical GPUs drawing tens to hundreds of watts, the difference is significant. For edge devices that are battery-powered, low-power, or always-on, Hailo’s low-power design reduces heat, power bills, and cooling needs.
Also, Hailo modules are very compact (M.2 modules or small PCIe cards), ideal when space is limited. This makes Hailo suited for energy-efficient, compact, and quiet inference — ideal for edge deployment in embedded systems, smart cameras, drones, robotics, IoT gateways, and remote devices.
Cost Efficiency: Higher TOPS per Dollar
Because Hailo is purpose built for inference rather than general compute tasks, it offers high cost efficiency (TOPS per dollar) compared to GPUs. In practical terms, this means for the performance you need (object detection, video analytics, pose detection) a Hailo-based setup may cost far less than a GPU-based one, while delivering comparable or even better inference performance. This is especially important when projects are budget-constrained or aim for large scale deployment.
Moreover, Hailo does not require expensive cooling solutions or heavy power infrastructure, further reducing the total cost of ownership.
Real-Time Low-Latency Inference Optimized for Neural Networks
Unlike GPUs which are general-purpose parallel processors designed for a wide variety of computing tasks, Hailo architecture is optimized specifically for deep neural network inference. Because of this domain-specific design, Hailo accelerators often deliver lower latency, faster inference per watt, and higher throughput per watt compared to GPUs — especially for tasks like object detection, video analytics, semantic segmentation, pose estimation, and multi-stream video inference.
This makes Hailo-based systems ideal for real-time applications such as surveillance, retail analytics, smart-city cameras, robotics, and other time-sensitive tasks.
Scalability and Flexible Form Factors: Easy Integration in Different Devices
Hailo offers a range of form factors: M.2 modules, mini PCIe, and PCIe cards, making it easier to integrate into various kinds of edge devices — embedded PCs, network video recorders, industrial computers, small form-factor boxes, robots, and drones.
For heavier workloads, you can also leverage higher-capacity PCIe cards or multi-module setups for higher performance. This flexibility lets you choose the right accelerator for your use case from entry-level to high-performance without redesigning your entire hardware.
Suitability for Harsh and Embedded Environments: Industrial and Automotive Grade Reliability
Hailo‑8 supports commercial, industrial, and some automotive-grade temperature ranges. This makes Hailo reliable for use in difficult environments such as outdoor, industrial, automotive, or IoT installations where conditions may be harsh and maintenance limited.
Because Hailo modules are compact and generate little heat, they are easier to embed in ruggedised devices, drones, robotics, or sensors where space, heat dissipation, and power are constrained.
Faster Development and Deployment: Easier Model Porting and Integration
Hailo supports popular AI frameworks such as TensorFlow, PyTorch, ONNX, and Keras so developers can port existing neural network models to Hailo without rewriting them from scratch. This reduces development time and complexity and accelerates time to market. For system integrators and startups building many edge AI devices, this is a big advantage.
Because integration is relatively straightforward and hardware requirements are minimal, deploying Hailo-based devices becomes simpler and more cost-effective overall.
When Hailo is the Right Choice: Typical Use Cases
Given all these strengths, Hailo-based edge AI is especially suited when:
You need real-time inference on video streams such as surveillance, traffic cameras, smart city video analytics, and smart retail analytics.
You are building battery-powered, embedded, or portable devices such as drones, robots, mobile AI devices, or smart sensors.
You want to deploy AI at large scale where cost and power efficiency matter.
The deployment environment is harsh or uncontrolled requiring compact design, reliability, and low maintenance.
You need to upgrade or retrofit existing systems without major redesign.
You prefer lower total cost of ownership and simpler operations with minimal power and cooling needs.
When GPUs Might Still Make Sense: Understanding the Tradeoffs
GPUs are not obsolete. In some scenarios, they remain a valid choice:
If you need to train very large-scale deep learning models, not just inference.
If your application needs general-purpose compute beyond AI inference such as video rendering, heavy compute workloads, or simulations.
For workloads requiring very large memory, very large models, or highly dynamic tasks.
If you already have GPU-based infrastructure and run edge AI on powerful edge servers where form factor or power constraints are manageable.
The decision depends on your use case, constraints such as power, size, or cost, and performance requirements.
Why Hailo is a Smart Investment: Long-Term Perspective
As edge AI adoption grows in smart cities, surveillance, IoT, industrial automation, robotics, and autonomous machines, demand for compact, power-efficient inference hardware will increase. Hailo meets this demand because of its edge-optimized design.
Hailo offers a modular approach from entry-level to high-performance allowing businesses to scale smoothly without redesigning hardware. Lower power consumption reduces operational expenses, and industrial-grade reliability reduces maintenance overhead. For large deployments, this translates into lower total cost of ownership.
Support for major AI frameworks allows reuse of existing AI model pipelines, reducing development effort and speeding up time to market.
Conclusion: Hailo AI Accelerators Are Ideal for Real-World Edge AI
Edge AI brings intelligence closer to where data is captured — cameras, sensors, robots, and gateways. For this, you need efficiency, compactness, reliability, and affordability.
Hailo AI Accelerators, especially Hailo‑8 and Hailo‑8L, deliver up to 26 TOPS in a small, power-efficient package with flexible form factors and compatibility with popular AI frameworks. They are ideal for real-world deployments in surveillance, video analytics, robotics, IoT, smart retail, industrial automation, and autonomous systems where power, size, or cost constraints exist.
While GPUs remain relevant for heavy training or general compute workloads, for edge inference and large-scale deployment, Hailo is often the smarter choice.
Top comments (0)