AI is starting to revolutionize industries and our personal lives, but the reliance on cloud-based processing has posed challenges.
For all the processing-scale benefits the cloud provides, there are three major challenges to leveraging that approach latency, bandwidth constraints, and security. It can take time and cost money to shunt vast amounts of data captured at the edge to the cloud for processing and then send it back out to be acted on at the edge. Some applications require lower latency for safety or accuracy reasons and so they’re looking for solutions outside the cloud. And many applications generate vast amounts of data, which can pose a security risk. Consumers especially aren’t fond of sending personal information – facial images, finger scans, voice data and the like – to the cloud when there’s a risk it can be hijacked along the path.
Enter Edge AI. Edge AI brings computational power closer to the data source, minimizing reliance on cloud connectivity and addressing latency, bandwidth bottlenecks and security challenges of cloud-based processing. And it’s capturing the imaginations of developers everywhere, who see vast opportunities to deploy solutions out at the edge paired with the right amount of processing power.
But Edge AI is early in its evolution, and it has its own challenges. Because it’s such a promising and exciting sector, countless players are entering the space, and this can cause technological fragmentation and compatibility issues in the absence of standards. Power consumption and cost are also important factors that need to be addressed for edge AI to scale quickly.
To help developers navigate this complex but promising landscape, the technical analysis and collaboration platform Wevolver, working in conjunction with Arm and more than a dozen other technology companies, industry experts and academics, has produced a comprehensive guide to Edge AI, its challenges and opportunities.
The nearly 60-page edge AI report walks you through opportunities in various industries, use cases, Edge AI platforms, how to make informed decisions on hardware and software choices, TinyML, real-world case studies and much more.
It’s a masterclass in the state of Edge AI today and vital for any engineer or developer who aspires to drive innovation at the edge.