Edge AI: The Powerful Convergence of AI in IoT and High-Performance Computing Applications at the Edge
Over the last decade, as connected devices proliferated and their capabilities expanded, there grew a need for real-time decision making independent of the cloud or connectivity in some cases. The move to a distributed architecture, driven by the transition of computational capacity from the cloud to the edge, ushered in a new era of edge computing.
There are several fundamental advantages of edge computing, including varied connectivity and data mobility options, the ability for real-time decision making, localized compute power and dynamic storage and security capabilities. We see those benefits increasingly reflected across a broad spectrum of applications across automotive such as autonomous driving / driver assistance features and industrial such as logistics and factory automation.
Data created by these endpoint devices is expected to grow at a staggering 85% with total data generated by IoT devices at reach roughly 80ZB in 2025 (Source: IDC, Future of Industry Ecosystems: Shared Data and Insights). For context, Zetta has 21 zeroes! Taking this a step further, approximately 90% of all data collected by enterprises today will never be used and is considered “Dark Data.” The bottom line is that data is data until we do something with it. And this is where AI/ ML becomes so powerful and meaningful.
AIoT —Artificial Intelligence of Things—is a Megatrend, driven by a perfect alignment of IoT, AI and 5G (3 powerful technologies) maturing roughly at the same time. This convergence will be transformational as intelligence concentrated in the cloud moves to the edge. To enable intelligence at the extreme edge and endpoints in the network, it must be done in a way that is highly efficient, responsive in real-time and cost effective. A decentralized intelligence model bridging the cloud and the edge has tremendous technical and economic benefits.
So, where are the opportunities for the semiconductor industry?
- AI semiconductors are expected to grow at 18% CAGR (2017-25) —five-times faster as compared to non-AI semiconductors (Source: McKinsey & Company, Artificial-intelligence hardware: New opportunities for semiconductor companies).
- Existing chips will see growth due to AI, but chips with innovative AI accelerators will experience most growth.
- AI technology stack will open many opportunities for semiconductor companies.
- There is a potential for much higher value capture from the technology stack for semiconductor companies, as compared to value captured historically from PC and mobile era.
- We expect a value-pull though enablement of end-to-end solutions for specific industries or “micro-verticals.”
Join us for a webinar on October 25 to learn more about market dynamics, opportunities for the industry, and practical use cases across industrial, consumer, and automotive end-markets.