Edge AI is transforming digital experiences as we know it. By infusing artificial intelligence into edge devices and networks, it unlocks game-changing capabilities for businesses worldwide.
As a data analyst and AI enthusiast, I find this technology fascinating. In this guide, we‘ll explore what exactly edge AI is, how it works, key benefits, real-world applications, and more. Let‘s get started!
What is Edge AI?
Edge AI refers to artificial intelligence models and applications deployed on edge devices or servers, at the periphery of networks. This allows data processing and intelligence right at the source of data.
Instead of solely relying on the cloud, edge AI enables smart devices and systems to analyze data and make decisions in real-time.
Key capabilities unlocked by edge AI include:
- Image and speech recognition
- Natural language processing
- Anomaly and pattern detection
- Predictive analytics and forecasting
- Real-time recommendations
- Autonomous decision making
This is made possible by leveraging technologies like deep learning, neural networks and reinforcement learning – the same technologies powering many cutting-edge AI applications today.
So in a nutshell, edge AI brings the core competencies of AI to the edge, supercharging devices with capabilities that used to be confined to the cloud.
The edge can refer to any computing resources located near data sources and endpoints. This includes devices like smartphones, embedded systems, IoT sensors, gateways, routers, shops, factories or hospitals.
Edge AI expands the boundaries of what advanced analytics and automation can do by overcoming geographic and connectivity constraints.
How Does Edge AI Work?
Edge AI relies on deep neural networks that are trained to mimic human intelligence for specific tasks.
These networks undergo extensive training using large datasets and computing resources in the cloud and data centers. Once trained, the models are then optimized, compressed and deployed to edge devices.
On the device, embedded analytics software uses the AI models to analyze real-time data from sensors, cameras, microphones or other inputs.
Common steps in an edge AI workflow are:
- Data is captured by edge sensors, devices or systems
- Devices pre-process and normalize data
- Embedded AI model analyzes the data and makes inferences
- Devices take actions based on model outputs
- Model accuracy improves over time through continuous learning
By performing analytics at the edge, near data sources, edge AI delivers results with extremely low latency. For example, an edge AI security camera can instantly detect intruders, while an industrial robot can monitor defects in real-time.
And with embedded machine learning capabilities, the edge devices can keep improving autonomously based on real-world data without relying on connectivity.
Cisco predicts that by 2022, 75% of enterprise data will be processed at the edge rather than corporate data centers or the cloud. Edge AI is what will catalyze this monumental shift.
Why is Edge AI a Game Changer?
Here are some of the many benefits driving massive momentum and investments in edge AI:
1. Blazing Fast Performance
By processing data and running AI models locally on devices, edge AI delivers near-instantaneous results. For many emerging applications like autonomous robots, industrial automation, and medical devices, millisecond latency is indispensable. Traversing hundreds of miles to the cloud simply takes too long.
2. 24/7 Availability
Edge AI allows devices to function reliably even with intermittent connectivity or when offline. This makes it ideal for remote, mobile or portable use cases ranging from construction sites to offshore rigs.
3. Enhanced Privacy and Security
With data processed on-device, edge AI reduces privacy risks associated with transmitting data to the cloud which could be vulnerable. Edge systems are also more robust to network disruptions and cyberattacks.
4. Lower Costs
Less data transmission means you save on networking costs. Processing data at the edge also lightens the load on cloud infrastructure, reducing cloud compute costs.
5. Easier Adoption
Companies can start small with edge AI at a few sites, and scale up easily by expanding devices. It also integrates easily with existing infrastructure and cloud services.
Specialized AI chips optimized for edge computing have a smaller footprint and lower power needs compared to energy-hungry cloud data centers.
These benefits are driving accelerating adoption across industries. According to IDC, the edge AI chipset market is forecasted to reach $9.8 billion by 2025, at a CAGR of 30%.
Next, let‘s explore some common edge AI application areas.
Edge AI Use Cases
Edge AI is bringing groundbreaking improvements across a diverse range of sectors:
Edge AI on cameras and sensors along assembly lines allows for real-time quality inspection, predictive maintenance, employee safety monitoring and more. This leads to lower defects, reduced downtime and optimized production.
- GE uses edge AI in its factories to detect anomalies in production early, avoiding losses of ~$400 million per year.
In-store edge AI allows retailers to continuously monitor inventory, detect sales trends, analyze customer demographics and traffic, enable self-checkout, recommend products, and deliver personalized promotions.
- Walmart saw a 20-30% increase in ON-shelf availability after deploying edge AI cameras in its stores for real-time inventory tracking.
Self-driving vehicles use on-board edge AI for object detection, lane tracking, navigation, speed optimization and more – enabling true autonomous operation without relying on connectivity.
- Tesla vehicles are equipped with powerful AI chips to enable full self-driving capabilities.
Edge AI is being deployed in medical devices for real-time patient monitoring, medical image analysis, early anomaly detection and alerts, personalized treatment recommendations and more.
- Philips uses edge AI algorithms to allow its MRI scanners to adapt protocols in real-time based on patient biometrics and movement.
Drones & Robotics
DJI‘s drone-mounted cameras use edge AI for obstacle avoidance, target following, filming stabilization and compliance with flight restrictions like GEO zones – all without an internet connection.
Mobile & Portable
Phones, wearables and portable medical devices use specialized edge AI chips to enable real-time translation, virtual assistants, fitness tracking, fall detection and other features while preserving battery life.
- Apple‘s A11 Bionic chip uses on-device deep learning for Face ID, Animoji and other features on iPhones.
These examples demonstrate the immense potential of edge AI across sectors to deliver the next wave of automation, intelligence and digital experiences.
Architecting Scalable Edge AI Solutions
To build edge AI solutions that can grow over time, architects need to consider these key elements:
Flexible Hardware Options – Supporting a variety of edge hardware configurations from embedded devices to microservers allows extending capabilities to new use cases faster.
Shared Libraries – Reusable libraries of optimized AI algorithms that can be deployed across edge locations reduces duplication of efforts.
Robust Management – Centralized visibility into all edge devices for monitoring, updates and control minimizes IT overhead as deployments scale.
Hybrid Architecture – A mix of edge AI and cloud AI enables balancing real-time performance with broader analytics. Data pipelines should allow seamlessly moving data from edge to cloud.
Enterprise Integration – Integrating edge data and insights with existing business applications and data lakes maximizes value. APIs and microservices help.
Security First – Multilayered edge security encompassing data, models, devices and networks prevents intrusions as attack surfaces grow.
With careful attention to these elements, enterprises can progress from edge AI proof-of-concepts to full-scale production leveraging hundreds of edge nodes.
Should You Develop In-House or Buy Edge AI Platforms?
When strategizing edge AI capabilities, companies have two options:
- Build in-house edge AI software using internal skills
- Leverage commercial off-the-shelf edge AI platforms
In-house development allows full customization and IP ownership, but requires significant investments in specialized skills and experience. It can also take too long to meet urgent business needs.
On the other hand, ready-made edge AI platforms allow faster deployment and scaling. Leading options include:
- AWS IoT Greengrass – Extend AWS services to edge devices for local compute
- Microsoft Azure IoT Edge – Build edge logic and deploy AI models from cloud to devices
- Google Cloud IoT Edge – Manage edge devices and deploy TensorFlow Lite models
- FogHorn – Lightweight real-time edge intelligence software
- SambaNova – Optimized edge AI hardware and software solutions
The choice depends on the use case complexity, timelines, and availability of internal skills vs. vendor trust and support. For most enterprises today, platforms solve the quickest path to edge AI value. In-house skills can then be leveraged to customize and extend the solution for competitive advantage.
The Future of Edge AI is Bright
As devices get smarter, edge AI adoption will accelerate exponentially. Gartner predicts there will be over 64 billion edge AI chips in devices worldwide by 2026.
Edge AI proves that transformative technologies are not just about the cloud and data centers. The edge offers radically new possibilities to use AI where the data is, powering the next generation of intelligent apps and devices.
And this is just the beginning. With 5G enabling new edge capabilities and use cases, and AI chips getting smaller and faster, edge AI will revolutionize computing as we know it.
So are you ready to unlock the power of AI and data at the edge? What are your thoughts on the potential of edge AI? I‘d love to hear your perspectives!