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pruning AI startups

AI Hardware Startups Ready for Pruning

By Peter Clarke

What’s at stake:
With plenty of VC funding on offer in recent years to bankroll an AI gambit, entrepreneurial engineers have been only too happy to accept the cash and assume the risk. But where are the returns?

There are too many artificial intelligence and machine learning startups. The going is getting tougher. Consolidation and acquisitions are bound to follow.

Just how rocky the AI market has become is illustrated by early mover and well-funded startup Graphcore Ltd., which announced layoffs last week. The announcement follows similar cuts at Mythic AI made earlier in the summer. If these established AI pioneers are axing jobs, what are the prospects for the legion of smaller AI wannabees?

We’ve seen about a decade of development of hardware implementations for neural network acceleration and the resulting AI algorithms. There are now probably more than 100 hardware-oriented startups still active and trying to cash in on the biggest revolution in computation since the adoption of the von Neumann architecture in the 1950s.

The accompanying roster of AI startups, organized by founding year, lists 90 entrants. All are fabless, using foundries to manufacture chips, and many are incorporating AI architectures in FPGAs or looking to license designs as intellectual property. Usually in such domains, the software- and services-oriented startups dwarf the hardware cohort by a factor of 10 or 20 to 1.

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Nakul Duggal on stage at The Autonomous

Qualcomm’s Automotive Gambit: Doubling Down on Smartphones

What’s at stake:
Qualcomm senior vice president Nakul Duggal does not lose sleep over carmakers’ veiled threats about developing their own chips. Duggal says Qualcomm understands that different OEMs are at different levels of maturity. Chip suppliers jockeying to lead carmakers to next-generation EV/AV architectures must understand what customers want, and know every OEM’s development stage. That understanding will allow them to offer multi-generational and scalable SoCs tailored to customers’ requirements.

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in-memory computing and AI

Inside In-Memory Computing, and Why It’s Back

By Ron Wilson

What’s at stake?
In-memory computing, an old and controversial way of organizing computer hardware to minimize energy consumption and maximize performance, has never quite broken through into the mainstream, except in some very specific applications. But the needs of edge-computing AI may provide an opportunity for a unique embodiment of this architectural idea.

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vehicle electronic control

Can AI in AVs Go Beyond ‘Perception’?

By Junko Yoshida

What’s at stake?
Artificial intelligence is commonly used in providing the perception abilities to highly automated vehicles. Can AI also help AVs make safer decisions in planning and control? Anyone accustomed to deterministic algorithms based on control theories will be hard to convince. But Infineon has a radical rebuttal and has been arguing its safety case with stakeholders in the automotive industry.

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