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Report: Edge Computing for AI Will Drive Deep Learning Chipset Market to $66.3 Billion by 2025
According to a report released this week by research firm Tractica, the global market for deep learning chipsets will increase from its current size of $1.6 billion to $66.3 billion in 2026 -- or approximately 41 times its current size.
Edge computing (where, as Tractica puts it, "AI computation is done on the device") is expected to make up three-quarters of the total market opportunity, the company said in a summary of its report. The "balance" of the market will be in cloud and/or datacenter devices, but it cited mobile phones as a "major driver" of the edge market, along with industrial categories including "automotive, smart cameras, robots and drones."
According to Tractica, as for the actual chips, System on a Chip (SoC) accelerators will make up the largest area of that market in terms of volume, followed by application-specific integrated circuits (ASICs).
"In the past year, more than 60 companies of all sizes have announced some sort of deep learning chipset or intellectual property (IP) design," the summary of the report states. "Every prominent name in the technology industry has acknowledged the need for hardware acceleration of AI algorithms and the semiconductor industry has responded by offering a wide range of solutions."
"Tractica expects that 2019 and 2020 will be the years when a ramp-up in deep learning chipset volumes will take place and winners will begin to emerge," Principal Analyst Anand Joshi commented in a prepared statement.
More details on the report are available here.
About the Author
Becky Nagel serves as vice president of AI for 1105 Media specializing in developing media, events and training for companies around AI and generative AI technology. She also regularly writes and reports on AI news, and is the founding editor of PureAI.com. She's the author of "ChatGPT Prompt 101 Guide for Business Users" and other popular AI resources with a real-world business perspective. She regularly speaks, writes and develops content around AI, generative AI and other business tech. She has a background in Web technology and B2B enterprise technology journalism.