By Peter Clarke

What’s at stake:
Right now, AI/ML is most powerful driver of technology but there are already signs that its runaway success is unsustainable on energy consumption grounds. Can a novel technology from a startup open up new frontiers in the artificial intelligence and machine learning (AI/ML) sector and ultimately impact the leaders of the semiconductor industry?

AI has a problem: the energy it consumes. A UK startup called Literal Labs may have a radical solution.

The company reckons a mathematical curiosity called the Tsetlin machine could provide an approach to many AI applications that is up to 1,000-times faster than GPU-based training and up to 10,000-times more energy efficient than today’s neural networks. If such efficiencies can be deployed while fitting into the established technology ecosystem, it could disrupt market leaders and enable AI at the edge, which has largely been stalled up until this point.

Read More »Are Tsetlin Machines About to Reframe AI?

By Junko Yoshida

What’s at stake:
Before founding Oculi, Charbel Rizk was a designer of autonomous systems frustrated with computer vision systems available on the market. Traditional sensors, often built for human consumption, produce massive volumes of data, which result in systems needing more bandwidth and suffering from increased latency. Can Rizk convince other systems designers to embrace Oculi’s new vision architecture originally developed to fulfill Rizk’s own wish list?

Oculi, a Baltimore, Maryland startup, offshoot of a Johns Hopkins University research team, has developed a vision technology architecture in which sensing and processing both reside at the pixel level. The company calls it Sensing and Processing Unit (SPU).

Charbel Rizk, Oculi’s founder and CEO said in a recent interview with the Ojo-Yoshida Report, “My claim to the world is that we will always enable the lowest power, bandwidth, latency and ultimately cost computer vision solution with privacy.”

This is big talk among the many players in sensing and processing, all of them pursuing ultimate edge AI solutions in a broad range of embedded systems.

Read More »Oculi’s ‘Software-Defined’ Vision Sensor Is Fresh and Foreign

By Junko Yoshida

What’s at stake
Chiplets present a set of multi-layered, multi-faceted, multi-dimensional technology and business problems with no one-size-fits-all answer. Numerous startups are proposing various solutions to tackle the complexity of die-to-die interconnects.

For every player in the semiconductor supply chain – from chip designers and EDA tool vendors to semiconductor foundries, OSAT companies and the Babel of technology startups – the toughest challenge, arguably, boils down to how to connect chiplets.

Fortunately, startups such as Eliyan, Blue Cheetah and YorChip are poised to tackle the issues, each in its own way.

Read More »Chiplet Mission: Navigate Interconnect Complexity

By Junko Yoshida

What’s at stake?
With chiplets poised to disaggregate SoCs into tiny dies, companies have begun to generate new ideas, tools and “chiplet platforms” designed to put back together these small dies (chiplets) – horizontally or vertically – in an advanced form of system-in-package. It is almost ironic that the chiplet originally conceived to disaggregate SoCs in Lego-like blocks appears to be getting back into an integration race again.

DreamBig Semiconductor, based in San Jose, Calif., is one of the startups at the gate. It came to CES last week and unveiled an “Open Chiplet Platform” called MARS.

Read More »Chiplet: Let Integration Race Begin

By Junko Yoshida

What’s at stake: 
The chip industry’s trajectory changed forever when a wave of EDA companies sprang up in the 1980s and offered commercial tools to accelerate complex IC design. Traditionally reliant on internal expertise to improve process engineering at fabs, chipmakers might now be ready to embrace commercial tools developed for semiconductor production. 

Manufacturing is the new black in the chip industry.

Read More »SandBox Moving AI Tools Beyond IC Design — to Manufacturing

By Junko Yoshida

What’s at stake:
The human brain is known for its efficiency, storing and processing information as sparse representations. At any moment, only a small fraction of neurons are active. Neuroscientists believe they can map brain-inspired logic into algorithms, data structures and architecture running AI models so that they offer a recipe to put “AI power hog” on a diet. Undetermined is whether data scientists are willing to alter their brute-force compute-based AI practice, which appears increasingly unsustainable.

It’s not often you meet a mostly self-funded startup, much less one that, for 18 years, has played a long game in neuroscience research, before suddenly unveiling a commercial software product.

More significantly, the startup’s new product poses a direct challenge to the red-hot Large Language Model (LLM) AI market.

Read More »Numenta Steps into AI-Neuroscience Rift

By Junko Yoshida

What’s at stake:
Digital Twin is not a new concept. But by combining it with a new generation of AI models, MeetKai, a startup, is committing resources to creating effective interactive tools – to be used by factories, for example – for optimizing the production process and training engineers remotely and virtually.

Meet MeetKai, an AI-Metaverse tool company. Also, meet James Kaplan, MeetKai’s CEO, who dropped out of Harvey Mudd College in 2014 to run a private equity fund for Michael Milken, once a junk-bond villain.

Kaplan co-founded MeetKai in 2018 in L.A. with Weili Dai, Marvell Technology’s co-founder.

Read More »MeetKai Eyes on AI-Driven Digital Twin for Industrial Market

By Junko Yoshida

What’s at stake?
Talk of edge AI, particularly machine learning, has captivated the IoT market. Yet, actual consumer products with local machine learning capabilities, are rare. Who’s ready to pull that off? Will it be a traditional MCU supplier or an upstart — like Useful Sensors?

Tech jargons like “smart home” and “smart sensor” have been overused to the point where real value that might be delivered by the related technologies reaches most non-techie consumers largely as fog.

Why, for instance, would any sensible person fiddle with apps, options and swipes on a smartphone to turn off the light when there’s a simple switch within reach?

Read More »From ‘Smart’ to ‘Useful’ Sensors

By Junko Yoshida

What’s at stake?
Specsmanship – power efficiency, performance efficiency and support for a variety of algorithms – absolutely matters in assessing AI hardware. But what about accuracy loss that occurs when system companies port AI models, developed on GPU, to other types of hardware? This is DeepX’s niche.

Last week, Lokwon Kim, a founder of AI chip startup DeepX, entered a conference in Santa Clara, Calif. with swagger and audacity, fittingly — because DeepX was rolling out a family of AI accelerator chips that, Kim claimed, will deliver “AI everywhere, AI for everyone.”

Kim chose the Embedded Vision Summit for his coming-out party. DeepX grabbed the role of lead sponsor and secured a premium spot on the show floor — a marketing coup usually too expensive for startups.

Read More »DeepX Founder Aspires to Be ‘Morris Chang of Korea’

What’s at stake?
With broader adoption of Driver Monitoring Systems imminent, Nauto, a late-stage startup focused on safer commercial fleets and drivers, sees an opportunity to sell OEMs on its AI algorithms by fusing data derived from outward and inward cameras. Is Nauto hitting the market with the right technology (ADAS + DMS) at the right time?

Many automotive companies have embraced Advanced Driver Assistance Systems (ADAS), whose outward cameras monitors the street. Meanwhile, carmakers are adding Driver Monitoring Systems (DMS), with cameras watching human drivers’ behavior inside vehicles.

Neither system is designed to work together. ADAS and DMS are developed and supplied by disparate technology companies, forestalling the design of vehicles that can correlate data coming from outward and inward cameras.

Read More »Nauto Driven to Fuse Data from ADAS and DMS