What’s driving AI to the Edge?
Machine learning – and deep learning in particular – requires intensive compute power. Running ML in remote data centres or the cloud has – until recently – been the only option.
But the proliferation of IoT devices and the need for real-time responses have created a number of challenges for this centralized model.
Privacy and security
Securing personal data is a key priority for service providers – breaches incur customer dissatisfaction and financial penalties. By using voice interactions and video footage, smart homes, consumer devices, retail and smart city systems are continually developing smarter capabilities for facial recognition, intent prediction and emotion detection. As they learn more about people, they create more personal data.
Over the years, poor data security practices have led to many security breaches. This has led to new codes of practice becoming legal requirements across the world. For example, the UK is planning to be the first country to enshrine Secure by Design principles in law. Other countries are expected to follow.
Using proportionate security measures and keeping data local can simplify compliance with regulations and reduce the opportunities for privacy breaches while data is in transit or stored centrally.
Many IoT deployments use a narrow bandwidth technology, such as NB-IoT or LTE-M, for connectivity. The more devices and the more data they send, the greater the potential for bottlenecks. Even with a wider bandwidth technology, sending vast amounts of data from devices is inefficient and expensive – especially if much of the data is irrelevant (for example, security footage when nothing is happening).
Reducing the amount of data that needs to be sent to the cloud can save cost and improve performance.
Latency, reliability and availability
Performance can be critical to success. Lives may depend on a fast response, for example, in autonomous vehicles, robotic surgery or monitoring factory machinery. Even in less critical situations, good user experience relies on responsive devices, such as when speaking the wake word to a voice assistant.
Autonomous applications can perform faster and more reliably than they might if they rely on central cloud systems for instructions.
Many IoT devices can’t use mains power because they’re mobile, in locations without electricity or there are too many devices to connect to mains power. Battery-powered devices must use energy extremely carefully, particularly if the batteries can’t easily be replaced or recharged.
The energy required to transfer data can be an order of magnitude greater than that required for computation so it’s beneficial to process locally where possible – even if it’s only to filter out data that doesn’t need to be sent to the cloud.
The number of machine learning models in the cloud has grown fast, thanks to improvements in processor speeds and the advances of big data. But centralised systems aren’t always best placed to support the growing demands of heavy workload IoT applications, such as those in healthcare, manufacturing and transportation.
Developing intelligence at the Edge can help meet the growing need for more AI in homes, cities, factories, healthcare, retail, transportation, business and more.