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Is ARM’s IPO this year’s best AI play?

Is Arm’s IPO This Year’s Best AI Play?

By Ron Wilson

What’s at stake:
Arm’s public offering could be a chance to invest in the explosive growth of artificial intelligence. Or investors could be buying into a total misunderstanding of Arm and what it does — whether AI takes off or not.

No recent tech IPO has stirred as much discussion as Softbank’s plan to float about ten percent of Arm Ltd. on the US NASDAQ exchange. Although Softbank has quietly backed off from their initial suggestions about an offering price, the figures still imply earnings growth close to that of Nvidia. That is, astronomical.

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Is Arm's IPO Destined to Disappoint?

Is Arm’s IPO Destined to Disappoint?

By Peter Clarke

What’s at stake:
The semiconductor and broader financial markets want to draw a line under a couple of years of lackluster performance, caused by political turmoil and economic recession. If successful, an IPO by Arm – one of very few in recent quarters – could signal that confidence is ready to return, helping unleash pent-up demand for deals and technology sales optimism. Wall Street and the markets could be “off to the races.”

Arm Ltd. is set to go public but the transaction is dogged by controversy – just like almost everything about the semiconductor IP vendor in recent years. A successful debut will be cheered by Wall Street, Arm’s customers and other chip enterprises. If the IPO has to be pulled or priced low to gain support, however, it could signal that caution will prevail in the markets generally. Worse, still, SoftBank Group and Arm may be perceived as yesterday’s heroes.

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Untangling China, Decoding the West’s Response

Untangling China, Decoding the West’s Response

By Bolaji Ojo

What’s at stake:
The politics of Chinese technology investments remain nuanced. Western technology companies want a piece of its market but are wary of giving up IP and are even more concerned about the impact of their governments’ growing anti-China policies. But which reality is true: a China filled with minefields for Western technology companies or a merely assertive nation trying to increase its own global economic presence. Figuring out which will determine how the West fares in the world’s second-largest economy.

Gina Raimondo was in China this week, on an inevitable trip, and saying the unavoidable.

Having helped to nurture China into the world’s second-largest economy, the West finds itself unable to take its eyes off or have its hands fully on the communist nation. It can neither be controlled and structured the way America and its allies would like nor exploited for the kind of profits they once dreamed could be gained.

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Cloud Connected by Default

As Devices Get Cloud Connected by Default, What Must MCUs Do?

By Junko Yoshida

What’s at stake:

IoT used to be defined as the network of physical objects — “things” — embedded with sensors, software, and other technologies so they can connect over the Internet and exchange data with other devices and systems. But today, IoT is defined simply as: Getting data connected to the cloud. How, in this light, must MCUs change?

IoT encompasses myriad market segments and product categories. It covers consumer electronics, factory floors and infrastructure. It offers everything from doorbells, pet trackers and smart speakers to diabetes monitors, smart meters and smart cities.

All these IoT applications inevitably demand different types of wired/wireless network connectivity and protocols, while posing serious cybersecurity concerns. The IoT market, by definition, invites fragmentation and interoperability problems among products and applications, thus creating scaling problems for companies in the IoT space.

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optical interconnects silicon photonics

As Optical Networks Expand, Marvell Is In It For the Long Haul

By George Leopold

What’s at stake:
Cloud and telecommunications networks are groaning under the weight of ever larger data sets. Expanding optical networks are evolving to provide greater bandwidth and faster interconnects needed to move data from one facility to another. Optical chip makers and their networking equipment partners are leveraging emerging interoperability standards to help service providers super-charge their networks while avoiding vendor lock-in.

Cloud data center operators and stodgier but essential telecommunications carriers are beginning to see the light as they evolve their optical network architectures to address ever-growing bandwidth demands while extending the reach of those networks.

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Aftermath of Artificial Stupidity

I Left My Intelligence in San Francisco

By Junko Yoshida

What’s at stake:
Calif. Governor Gavin Newsom can remain a cheerleader for tech. San Francisco Giants can proudly wear a Cruise jersey patch. Cruise can continue to push its narrative that humans are terrible drivers. But at stake is the safety of a non-consenting public forced to share the roads with inscrutable machine drivers.

Bryan Reimer, research scientist at MIT Age Lab, recently linked me to a TED Talk he gave five years ago.

In it, he reminded the audience: “We almost always forget that our infrastructure was really designed and built for human drivers.”

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Transformers in Auto: Who Does it, Who Needs it?

By Junko Yoshida

What’s at stake:
Tech companies are promoting myriad claims that they have solutions to process transformer algorithms better than others. The benchmarking of transformer engines is not yet available.

The burgeoning trend toward generative AI has flipped the whole AI world on its head, or so it seems.

Large Language Models (LLMs), as seen in ChatGPT, are mostly limited to language modeling and text generation. But transformers – an overarching deep-learning architecture that underlines LLMs and other generative AI applications – offers a model useful in data streams ranging from text, speech and image to 3D and video, or any sensory data. 

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Over stuffed baggage

Handling the Baggage of Edge AI

By Junko Yoshida

What’s at stake:
Everyone loves talking about Edge AI, but without mentioning the persistent gap between the AI and embedded worlds. Edge AI designers are caught in a never-ending cycle of ‘optimization’, pressed to fit neural network models and achieve acceptable accuracy on their hardware. They are desperate for tools to lighten their load. At stake is the scaling of edge AI deployment.

Edge AI today stands at “this uncomfortable junction,” said Evan Petridis, CEO at Eta Compute, in a recent interview with the Ojo-Yoshida Report. Edge AI straddles two domains – machine learning (ML) and embedded. These two distinctly different fields share neither the same language nor design philosophies.

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