Edge AI shifts computational loads from the cloud to nearer the devices at its edge. This is in contrast to traditional AI computing where edge devices feed data to the cloud. The algorithms reside and compute insights in the cloud which are then passed back to the edge devices. Over a period of time this exchange of data and insight further refines the model and delivers accurate insights. Companies looking to harness the power of innovative technology practices can benefit from this new wave of AI computing. This guide defines Edge AI, explains its meteoric rise, details its benefits and uses, and forecasts future developments.
What is Edge AI?
Edge AI involves intelligent data processing and computation on a local device or edge-enabled devices near the data collection source and operating on the same network. Through Edge AI, data does not have to travel offsite to a cloud computing facility, private data center, or another storage center for AI computation. Instead, AI computation can occur on the device at the edge of the network.
Why is Edge AI on the rise?
Batteries power most edge devices. Hence they have been highly power efficient and had low computational capabilities that were adversely suited for AI computation. However, advancements in computational hardware are making edge devices more potent with the ability to offload a particular portion of the computational load away from the cloud and onto itself.
The demand for intelligent solutions that are almost intimate is matched by three recent innovations that power the technology.
- Machine Learning (ML), Deep Learning (DL), and neural networks have developed so extensively that companies can now successfully train AI models and deploy them into action at the Edge.
- Graphics Processing Units (GPUs) have advanced and adapted to execute neural networks and provide the powerful distributed computational power required for Edge AI.
- The rapid expansion of the Internet of Things (IoT) and the explosion of big data provides the devices and information necessary to power and deploy AI at the Edge.
Uses and Benefits of Edge AI
Edge AI is used in many applications, including:
- Self-driving cars
- Video surveillance cameras
- Image classification systems
- Health data analysis and preventative care
- Voice assistants
- Intelligent forecasting
- Predictive maintenance
The list of applications will continue to grow due to the significant benefits Edge AI delivers, including:
Reduces Overall Business Costs
With Edge AI, data does not have to travel offsite. By bringing the processing and data closer together, applications require less network bandwidth and capacity, and processing can occur faster. The reduction in network demand and acceleration of processing time both contribute to lower overall business costs.
Rapidly Expands and Enhances Insights
Edge computing is incredibly beneficial, and the AI capabilities elevate the advantages to an entirely new level. AI solutions are powerful and flexible because they are trained to process infinitely diverse inputs and continue to train as it processes more data. In fact, the longer an AI model is in production at the Edge, the more accurate and useful the model will be.
Reduces Risk and Increases Privacy
Edge computing delivers security benefits by reducing the distance data must travel for processing, analysis, and storage and increasing owners’ control over the data. When paired with AI, Edge technology also increases privacy because the data does not need to be exposed to a human being. Any data that is uploaded to the cloud can be anonymized to preserve privacy and adhere to compliance requirements.
The Future of Edge AI
Companies that want real-time data analytics will have to rely on Edge AI. Edge AI is expected to continue its meteoric rise with advancements in the Metaverse, Deep Learning, security, automation, and breakthroughs in countless industries.
To take advantage of the existing benefits and prepare to adopt innovative Edge AI solutions in the future, companies can partner with Encora. Encora is deeply expert in the various disciplines, tools, and technologies that power the emerging economy. This is one of the primary reasons clients trust Encora to lead the full Product Development Lifecycle. When it comes to Edge AI, Encora is involved in building several AI/ML models designed to run on edge devices with lower configurations, and Encora engineers help clients take advantage of the most advanced Edge AI technologies. Contact us to learn more about Edge AI and our software engineering capabilities.