The shape of edge AI to come – VentureBeat

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Its not often the world of semiconductors is turned on its head. Its clear that a similar transformation is occurring as a superabundance of start-ups takes on the challenge of low-power neural nets.

These start-ups are trying to move neural network-based machine learning from the cloud data center to embedded systems in the field to whats now called the edge. Making chips work in this new world will require new ways of setting up neurals, designing memory paths, and compiling to hardware.

Establishing this new formula will challenge the brightest heads in electrical engineering. But the push has begun for edge AI. Its spawned myriad startups, including Axelera.AI, Deep Vision, EdgeQ, Hailo, Sima.ai, and many more.

Driving this, according to analyst firm ABI Research, is the need for local data processing, low latency, and avoidance of repeated calls to AI chips back on the cloud. The firm also cites better data privacy as an impetus. Its all seen as an opening for upstarts in an edge AI chipset market that ABI estimates will grow to $28 billion in 2026, for a compound annual growth rate (CAGR) of 28.4% from 2021 to 2026.

That growth will require designs that move beyond bellwether AI apps, like those that recognize images of cats and dogs, created in power-rich cloud data centers. That quest to expand use cases should bring pause to optimists.

Making the chips is one thing, but getting them to work across many different neural network types is another. We are not there yet, said Marian Verhelst, a circuits and systems researcher at Katholieke Universiteit Leuven and the Imec tech hub in Belgium, as well as a member of the TinyML Foundation, who spoke with VentureBeat.

Still, its a really cool time to be active in this new domain, adds Verhelst, who is also an advisor to Netherlands-based Axelera.AI. The company recently gained $12 million in seed funding from security infrastructure provider Bitfury to pursue Edge AI chips.

What matters when it comes to designing this new chip generation? Chip designers and their customers alike now need to explore the question. In an interview, Verhelst outlined the pressing points as she saw them:

These matters drive design decisions at Axelera AI. The company is preparing to go to market with an accelerator chip centered around analog in-memory processing, transformer neural nets, and data flow architecture while consuming less than 10 watts.

We put together the in-memory computing, which is a new paradigm in technology, and we merge this with a data flow architecture, which gives a lot of flexibility in a small footprint, with small power consumption, said Axelera cofounder and CEO Fabrizio Del Maffeo, who emphasized that this is an accelerator that can work with an agnostic assortment of CPUs.

Del Maffeo cites vision systems, smart cities, manufacturing, drones, and retail as targets for Edge AI efforts.

The competition to forge a solution in edge AI is tough, but entrepreneurs like Del Maffeo and engineers like Verhelst will enthusiastically accept the challenge.

Its a very interesting time for hardware, chips, designers, and startups, Verhelst said. For the first time in a couple of decades, hardware really starts to be at the center of attention again.

No doubt, its interesting to be there when a new IC architecture is born.

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The shape of edge AI to come - VentureBeat