The wait is almost over for the promise of artificial intelligence at the edge to become a reality – but a new market research report has warned that predicted growth would be ‘anything but steady.’
Market research firm IDTechEx, in its recent report, ‘AI Chips for Edge Applications 2024-2034: Artificial Intelligence at the Edge’, has predicted that the global market opportunity for AI at the edge will touch $22 billion USD (£17.4bn) by 2034.
The company cites the rollout of production-level technology by various AI chip startups targeting edge applications as key to future growth – and explores where AI sits across the wider technology and vertical landscape.
Yet the finger is pointed at two possible future scenarios. IDTechEx notes the ‘saturation and stop-start nature’ of certain markets that have either already employed AI architectures in their current chipsets or required rigorous testing for high volume rollout respectively. An example here is with smartphones increasingly incorporating AI co-processing in their chipsets. The ‘premiumisation’ of an already saturated market is now occurring meaning AI revenue will increase, though this will itself begin to saturate in the coming decade.
“The edge of the network, where users have immediate interaction with devices that do not necessarily rely on the cloud for computation, has been touted as something of the promised land for AI, where inclusion of accurate and somewhat autonomous AI would enable a true Internet of Things,” the company noted. “Instead, AI has slowly trickled into certain household devices and consumer electronics goods, with other applications yet to realise the full impact that AI has promised.”
So where do applications of edge AI really come through? The answer is in another recently-released report, this time from Wevolver. The report notes that edge AI ‘shines in applications where rapid decision making and immediate response to time-sensitive data are required’ such as autonomous driving, and healthcare.
IDTechEx notes that one element where the edge AI market will blossom, in the middle and towards the end of the decade, is in flagship System on Chips (SOCs) for advanced driver assistance systems (ADAS) from the likes of Mobileye and Qualcomm, due to hit automotive manufacturers’ production lines by 2025.
Wevolver explores the various challenges associated with edge AI, from data management, to integration, to security and scalability. The real-time data captured by edge devices can be incomplete or noisy, and real-time decision making has implications for power and computation, as well as the security and application level. From a scalability perspective, one issue with edge is the difficulty in scaling testing before development, particularly with an edge model running inside multiple devices and getting inputs from multiple sensors.
The Wevolver report is more optimistic than the IDTechEx analysis. “The importance of edge AI in the current and future technology landscape cannot be overstated,” the report concluded. “Its impact is felt across domains such as autonomous vehicles, healthcare, industrial automation, and IoT deployments.
“By bringing AI capabilities directly to the edge devices, edge AI empowers devices to operate autonomously, adapt to their surroundings, and make informed decisions in real-time.”
Yet IDTechEx, while more circumspect, agreed that such improved capability was a question of when, not if. “Though the types of models that are employed at the edge will be, in the main, much simpler than those handled within data centres, due to the power constraints of edge devices, it is only a matter of time before even the simplest of AI functions – such as hands-free activation and actioning – comes as an added feature to a range of devices, particularly within the home,” the company said.
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