What is Edge AI?
AI application deployment in devices all over the physical world is called Edge AI. As the name refers to Edge AI, computation of AI is conducted nearer the users at the network’s edge close to the location of the data, instead of placing it in the central facility of cloud computing or any other private centre of data. As the internet has the capability of global reach, the network’s edge is able to connote every location. Such as it can be a hospital, factory, retail outlets, or day-to-day devices around us, for instance phones, autonomous machines and traffic lights.
In contemporary times, almost every organisation, irrespective of their industry, has used automation to enhance their efficiency, processes and productivity with safety. In order to assist them, computer programs must identify patterns and implement tasks safely and repeatedly. However, the unstructured world comprises different tasks that individuals carry out to cover limitless circumstances that are impractical to describe in rules and programs entirely.
With the advancement of Edge AI, new possibilities and opportunities for devices and machines have opened up to operate with human cognition intelligence. Innovative AI-enabled applications perform the same tasks for different circumstances, similar to real life.
According to Statista, by 2025, ASIC (“application-specific integrated circuits), the share of the processing power of AI edge computing is projected to be 70%, whereas the CPUs will become obsolete by then.
The efficiency of AI model deployment at the edge sprung from recent innovations. These are:
Neural networks maturation:
AI infrastructure and neural networks have finally evolved to a fulcrum of permitting machine learning in general. Several organisations are figuring out ways to equip and train AI models to deploy them successful at the edge of production.
Advancement of computing infrastructure:
Computational power distributed to run at edge AI has advanced highly in parallel GPUs and has adapted to execute neural networks.
IoT device adoption:
In the current market powered by big data, IoT has entered with a bang to fuel this bombardment of constantly growing technology. The introduction of industrial-grade sensors, robots, smart cameras and more has been the backbone of the IoT-enabled devices. This sudden change helped the industry collect various data sets rapidly and deploy the AI models quickly at the edge. Lastly, the boost and stability provided by the 5G networks have been the cherry on top of this all.
Benefits of Edge AI
Even though AI algorithms have the ability to understand sights, sounds, language, temperature, faces, smells and other unstructured information’s analogue forms. However, they are only applicable to real-world end-user issues and impractical to deploy centralised enterprise data or cloud because of bandwidth, privacy and latency problems. It is here where edge AI steps in, as it provides:
- Real-time insights
- Increased privacy
- Reduced cost
- Persistent improvement
- High availability
Practical Implementation of Edge AI
Edge Ai is being increasingly used in increasing real-world uses. Such as in areas of:
- IoT-powered AI devices in healthcare
- AI-powered energy forecasting
- Automated predictive production in manufacturing
- Smart virtual assistance in the retail sector
Why Edge AI is the future?
The IoT explosion in recent times has changed the entire real state of AI. All of the maturation in the sector of neural networks and factors such as parallel processing with stable 5G has now provided us with a structured and organised infrastructure for machine learning at all levels. The computational prowess provided by edge AI with the help of the aforementioned factors will allow the organisations to build and capitalise on this newfound power provided by edge AI. However, we could say that we are in the early stages of edge AI, but the future looks promising.
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