VC slowdown has yet to hit AI chip designers • The Register

Analysis Some AI chip startups are managing to raise capital from investors despite operating in a crowded market where venture capital funding has plummeted in the past year.

Venture-backed AI chip designers who have raised funding rounds in recent months include Israel-based companies NeuRealityHolland to axel as much as, quadricY load AIthree American companies based in Silicon Valley.

These startups convinced investors to part with tens of millions of dollars in an investment environment that is much more conservative given this year’s shaky economy, pushed by inflation and rising interest rates.

What Register recently reportedGlobal VC funding for semiconductor startups in 2022 decreased 46% to $7.8 billion as of December 5, reflecting increased scrutiny of these capital-intensive companies.

While funding has plunged deeply for semiconductor startups this year, the decline in the total number of known funding rounds hasn’t been as steep, falling 20 percent in 2022 to 618 deals.

Of the five startups that have raised recent funding rounds, four of them (Axelera,, Quadric, and EnCharge AI) are focused on AI chips to run inferences on edge devices, where data is processed at high speeds using the lowest amount of energy. possible in restricted form factors is paramount.

That may not be a complete match. As of October 31, venture capital funding for inference-based AI chip designers had surpassed money raised by processor-focused startups for training, the first step in AI application development before resorting to inference for its implementation in the real world. That’s according to a November research note written by PitchBook senior analyst Brendan Burke.

Burke said this is a reversal of a trend seen over the past four years, where funding for chip startups focused on training or training and inference outpaced capital for those working only on inference.

“There is a cyclical component to this pattern, given the limited need to train companies to raise funds each year; however, we see that inference-focused companies are achieving significant business partnerships during the economic downturn,” he said.

This increased need for inference chips coincides with a projected growth in spending on edge-case AI chips, a rather confusing term that, in the case of the Pitchbook report, includes the PC, automotive, and industrial markets. Burke said the PC and automotive markets for AI chips grew more than 22 percent in 2022, faster than spending on that silicon for data centers.

“Demands for automotive and edge computing are driving more commercial deals for inference-focused chips than training chips in the cloud,” the analyst added.

Not all startups working on AI chips have been lucky this year.

Mythic, a Texas-based startup that was developing analog chips for advanced artificial intelligence use cases, ran out of investor funding before it could generate revenue, a senior executive said. said in November.

Then there’s Graphcore, a British startup that has commercialized data center chips for training and inference. The company reportedly its private valuation was cut by $1 billion after losing a key deal with Microsoft, among other financial woes.

Ruta Belwalkar, private investor and chip designer, previously told us she wouldn’t be surprised if more chip-design startups ended up being acquired or shut down in the next year because they failed to transition quickly enough from research and development to commercialization.

It’s not just investors who are finding some AI chip startups still acceptable in a weaker economy. Some industry veterans are also taking the plunge.

For example, Lightelligence, a Boston-based optical AI chip startup, recently hired Weifeng Zhang, a former chief scientist for heterogeneous computing at Alibaba, along with Wayne Wu, the previous head of AMD’s PCIe design team, and Hal Conklin, who was most recently vice president of worldwide channel sales at Arm.

To them, we say good luck. ®

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