However, while 2021 will be remembered as an exceptional year, it is clear the funding environment has changed. The transition from a global chip shortage to an inventory crisis, the turning point in monetary policy, and the economic downturn in 2022 mean it is now more challenging to raise funding.
"The best-funded AI chip startups are under pressure to deliver the kind of software support developers are used to from the market leader, NVIDIA", Omdia Principal Analyst for Advanced Computing, Alexander Harrowell, notes. "This is the key barrier to getting new AI chip technology into the market."
Omdia expects that more than one major startup is likely to exit this year, possibly through a trade sale to either a hyperscale cloud provider or a major chipmaker. "The most likely exit route is probably via trade sales to major vendors," says Harrowell. "Apple has $23bn in cash on its balance sheet and Amazon $35bn, while Intel, NVIDIA, and AMD have some $10bn between them. The hyperscalers have been very keen to adopt custom AI silicon, and they can afford to maintain the skills involved."
Omdia also found that half of the $6bn in VC funding over this time period has been directed into only one technology – large-die, CGRA accelerators, which is often designed with the aim of loading entire AI models on-chip. However, there are questions about this approach given the continuing growth of AI models.
"In 2018 and 2019, the idea of bringing the entire model into on-chip memory made sense, as this approach offers extremely low latency and answers the input/output problems of large AI models. However, the models have continued to grow dramatically ever since, making scalability a critical issue. More structured and internally complex models mean AI processors must offer more general-purpose programmability. As such, the future of AI processors may lie in a different direction," concludes Harrowell.
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Fasiha Khan / +44 7503 666806 / Email Contact / www.omdia.com
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