WHAT IS EDGE AI?
Edge AI, short for Edge Artificial Intelligence, is the practice of running AI models directly on devices like smartphones, cars, wearables, and smart home hubs without relying on distant cloud servers. This decentralised approach enables real-time decision making, lower latency, and increased data privacy. Edge AI is critical for applications where speed, bandwidth, and privacy are essential, such as autonomous driving, health monitoring, and industrial automation.
Edge AI allows for real-time data processing and decision-making closer to the source of data generation. This is made possible by increasingly powerful AI processors for edge computing, which enable sophisticated models to run on compact, energy-efficient devices.
Edge AI is growing in popularity as more AI-capable hardware becomes available and fast, accurate AI models emerge that are efficient enough to run on everyday devices. Estimates on the speed of rollout vary. Counterpoint Research suggest that by 2028 54% of mobile edge devices will be AI capable. Meanwhile, research from the SHD Group gives a lower estimate of 31% penetration by 2030. It seems that everyone agrees on one thing: Edge AI is on the rise.
KEY ADVANTAGES AND BENEFITS OF EDGE AI
One of the primary benefits of Edge AI is its low latency, or its ability to process data closer to real-time than cloud AI. By performing computations locally, edge devices can respond to events almost instantaneously, which is crucial for applications such as autonomous vehicles, industrial automation, and healthcare monitoring.
To understand the difference between Edge AI and Cloud AI, view our “Edge AI vs Cloud AI” comparison article.
What is more, Edge AI systems can continue to operate even in the absence of a stable internet connection. This resilience is particularly important in remote or challenging environments where connectivity may be intermittent or unreliable. Autonomous vehicle technology is a simple example of an edge device that needs to handle much of its processing locally: a self-driving car cannot simply stop when it loses network coverage!
Processing data locally on edge devices reduces the risk of data breaches and unauthorised access. Sensitive information, such as personal health data or financial transactions, can be analysed and acted upon without leaving the device, ensuring greater privacy and security.
Edge AI hardware and edge inference models are designed to be far more efficient than their cloud-based equivalents. They have fewer parameters and use techniques like quantisation and sparsity to deliver high accuracy results with much lower power consumption. This efficiency carries through into network usage. Edge AI minimises the need to transmit large volumes of data to the cloud for processing – and then transmit the results from the cloud back to the device. This reduction in data transfer results in a more efficient total solution and alleviates network congestion.
Finally, Edge AI enables the deployment of AI capabilities across a vast number of devices, creating a distributed network of intelligent nodes. This scalability allows organisations to expand their AI applications without massive investments in centralised cloud resources.
REAL-WORLD EDGE AI APPLICATIONS ACROSS INDUSTRIES
An Edge AI application that almost everyone will have experienced is an advanced driver assistance system (ADAS) in the automotive market. In many of today’s cars there is an element of cruise control, lane detection, surround view applications, collision avoidance and more. Edge AI technology enables the real-time processing of sensor data for these tasks. The low latency and high reliability of Edge AI are essential for ensuring the safety and efficiency of self-driving cars, and efficiency is important when it comes to not over-consuming a vehicle’s battery.
AUTOMOTIVE & ROBOTICS
Embodied AI · Multi-sensor fusion · Environmental understanding · Predictive modelling · Path planning
MOBILE & CONSUMER
Computer vision · Enhanced photography · Natural language processing · Virtual assistants
EDGE COMPUTING & IoT
Predictive maintenance · Real-time monitoring · Event detection · Distributed intelligence · Autonomous decision making
Elsewhere, in manufacturing and industrial settings, Edge AI is used for predictive maintenance, quality control, and process optimization. By analysing data from machinery and equipment within the equipment itself, Edge AI can detect anomalies, predict failures, and optimize production processes immediately, leading to increased efficiency and reduced downtime.
In healthcare, Edge AI is transforming patient experiences through applications like remote monitoring. Health information is highly sensitive and patients are wary of sharing this data into the cloud. Furthermore, it is simply unnecessary to stream such data into the cloud when a simple wearable can continuously monitor the feed, detect anomalies and notify the patient.
In towns, Edge AI is a key enabler of smart city initiatives, where it can be used for traffic management, energy optimization, and public safety. For example, smart traffic lights equipped with low-latency AI solutions can adjust their timing based on real-time traffic conditions, reducing congestion and improving traffic flow. Edge AI is practical for use cases like this: it means the device doesn’t need an internet connection, the workloads can easily be executed on-device, and doing so reduces the total system cost of delivering this sort of experience by lowering network traffic and server usage.
CHALLENGES OF IMPLEMENTING EDGE AI ON DEVICES
From a hardware perspective, edge devices need to have enough performance to deliver an acceptable user experience when processing complex AI models locally. This performance can be judged by both time to first token (how long it takes for the first word to appear on a screen when running a large language model) and tokens per second (once that first word has appeared, the speed at which the subsequent words flow).
This performance needs to be delivered within significant power and area constraints. Many edge devices are battery-operated embedded platforms, making AI in embedded systems a careful balancing act between performance, power, and thermal constraints.This means that system designers must focus on energy saving solutions like efficient data management to preserve battery life; excessive power usage can also introduce heat issues and potential thermal throttling in non-cooled devices. From an area perspective, silicon is expensive and even an extra 5mm2 on a design can make or break its cost-effectiveness. Like with power, this forces system designers to be creative when it comes to things like data management to reduce the amount of area spent on memory.
On the software side, deploying an AI application across the edge can be a complex and time-consuming project. Edge devices are not homogeneous; unlike the cloud whose processors are fairly consistent. The diversity of processors and vendors increases the challenge when it comes to software portability and performance optimisation. Some processors are more programmable than others – NPUs, for example, are much more difficult for third party developers to code for than GPUs and CPUs which have far more established software ecosystems.
More information on the Challenges of Edge AI.
WHY THE FUTURE OF AI IS AT THE EDGE
As technology continues to evolve, on-device AI will become increasingly common across sectors that require responsiveness and reliability. Edge AI represents a paradigm shift in the way AI and data processing are approached. By bringing intelligence closer to the data source, Edge AI offers numerous advantages, including low latency, reduced bandwidth usage, enhanced privacy, and improved reliability. Its applications span various industries, from autonomous vehicles and industrial automation to healthcare and smart cities. However, the successful implementation of Edge AI also requires addressing challenges related to hardware limitations, model management, security, interoperability, and cost. As technology continues to evolve, Edge AI is poised to play an increasingly important role in shaping the future of intelligent systems and applications.
Imagination is at the forefront of Edge AI hardware innovation. Explore our latest AI processors and discover how we empower on-device intelligence across automotive, consumer, and industrial edge systems.
