AI at the edge: 5 trends to watch
Edge AI offers opportunities for multiple applications. See what organizations are doing to incorporate it today and going forward.
AI at the edge continues to develop. AI applications on the edge are myriad: Autonomous vehicles, art, health care, personalized advertising and customer service could all make use of it. Ideally, edge architecture delivers low latency on account of being closer to the requests.
Astute Analytica predicts the edge AI market will grow from $1.4 million in 2021 to $8 million by 2027, a CAGR of 29.8%. They expect this growth will come in large part from AI for the Internet of Things, wearable consumer devices and a need for faster computing in 5G networks, among other factors. These bring both opportunity and reservation because edge AI’s real-time data is vulnerable to cyberattacks.
Take a look at five trends likely to shape the field of edge AI in the next year.
One of today’s sea changes is the ability to run AI processing without a cloud connection. Arm recently released two new chip designs which can bring processing power to the edge for IoT devices, skipping either a remote server or the cloud. Their current Cortex-M processor can handle object recognition, with other abilities such as gesture or speech recognition coming into play with the addition of ARM’s Ethos-U55. Google’s Coral, a toolkit to build products with local AI, also promises hefty AI processing “offline.”
NVIDIA predicts that best practices in machine learning operations will prove a valuable business process for edge AI. It needs a new lifecycle for IT production — or, at least, that’s the speculation as MLOps develops. MLOps could help organize and push the flow of data to the edge. A continuous cycle of updates may prove effective as more organizations find out what works best for them when it comes to edge AI.
Data scientists working on designing algorithms, choosing the model architectures and deploying and monitoring ML on a day-to-day basis may benefit from simplified ML methods.
That means “it’s possible for neural nets to design neural nets,” said Google CEO Sundar Pichai.
Auto ML requires a lot of memory and computational power, so its deployment at the edge goes hand-in-hand with other ongoing processing considerations.
In order to do more processing on the edge, companies need custom chips to deliver sufficient power.
