Startup looks to speed machine learning with NOR arrays -

Startup looks to speed machine learning with NOR arrays


AUSTIN, Texas — Wedged between a coffee shop and a hair salon in a gentrifying suburb here, a couple dozen engineers are exploring a new direction in computing. Startup Mythic aims to map neural networks into NOR memory arrays, calculating and storing results in ways that shave power consumption by perhaps two orders of magnitude.

If it works, the startup could leapfrog digital processors and cores from the likes of Intel, established IP providers, and a handful of well-heeled startups in China. They all aim to fill sockets in next-generation surveillance cameras, drones, factory gear — all sorts of embedded systems trying to hop on the bandwagon to artificial intelligence, including someday self-driving cars.

“We had known from grad school that mixed-signal processing was a great fit for this app,” said David Fick, who launched the company with a colleague at the University of Michigan. “You need to store a lot of weights and flash memory with its adjustable threshold voltage — every transistor is very appealing.”

The flash arrays essentially eliminate the need for moving data in and out of external memory, slashing power consumption. Mentors David Blaauw and Dennis Sylvester “had started some flash research and we had some expertise, so we could pretty easily spin up a project,” said Fick of his work with co-founder Mike Henry.

But executing on the decades-old concept of an analog processor in memory is tricky. “You have to account for many analog effects — mismatch, noise, temperature, and memory cells have a similar amount of significant effects,” he said.

Unlike digital computers with well-defined memory, processing, and storage subsystems, analog computers for machine learning are essentially one big integrated behemoth.

“You need to co-design everything simultaneously, so you need people who understand overlapping sectors like device and neural network people who understand each other’s areas,” said Fick. “We’ve been a lot more successful in that than others with a great team that can do the whole stack.”

Indeed, the company snagged its first big investment, a $50 million Series B, in part because it pulled together a diverse team of director-level experts. They include an analog specialist from Texas Instruments, a flash design director from Microchip, and a physical design expert from Netronome.

Dave Fick in his Austin office with his dog, Ellie, the startup's unofficial director of emotional support. (Images: EE Times)

Dave Fick in his Austin office with his dog, Ellie, the startup’s unofficial director of emotional support. (Images: EE Times)

Showing stepwise progress with a series of prototype tapeouts also won over investors. Fick gives a lot of credit to VLSI work in college.

“When you design a chip as a grad student, you have to do memory, synthesis, DRC variations … all by hand. If you go straight to industry, you never see the whole process, so a lot of startups coming out of academia are more successful going to production.”

Both co-founders have been geeks from the start. Fick’s first job in high school was as a web developer. As a grad student, he had a string of internships at companies including AMD, IBM, and Intel. For fun, Henry used to enter speed-programming competitions.

Continue to page two on Embedded's sister site, EE Times: “Startup maps AI into flash array.”

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