MADISON, Wis. — Ren Wu, formerly a distinguished scientist at Baidu, has pulled a new AI chip company out of his sleeve, called NovuMind, based in Santa Clara, Calif.
In an exclusive interview with EE Times, Wu discussed the startup’s developments and what he hopes to accomplish.
Established two years ago, with 50 people, including 35 engineers working in the U.S. and 15 in Beijing, NovuMind is testing what Wu describes as a minimalist approach to deep learning.
Rather than designing general-purpose deep-learning chips like those based on Nvidia GPUs or Cadence DSPs, NovuMind has focused exclusively on developing a deep learning accelerator chip that “will do inference very efficiently,” Wu told us.
NovuMind has designed an AI chip that uses only very small (3x3) convolution filters.
This approach might seem counterintuitive at a time when the pace of artificial intelligence has accelerated almost dizzyingly. Indeed, many competitors concerned with yet-to-emerge AI algorithms have set their sights on chips that are as programmable and powerful as possible.
In contrast, NovuMind is concentrating on “only the core of the neural network that is not likely to change,” said Wu. He explained that 5x5 convolution can be done by stacking two 3x3 filters with less computation, and 7x7 is possible by stacking three. “So, why bother with those other filters?”
The biggest problem with architectures like DSP and GPU in deep-learning accelerators on edge devices is “the very low utilization” of their processors, Wu said. NovuMind solves “this efficiency issue by using unique tensor processing architecture.”
Wu calls NovuMind’s idea — focused on the minimum set of convolutions in a neural network — “aggressive thinking.” He said the mission of his new chip is to embed power-efficient AI everywhere.
The company’s first AI chip — designed for prototyping — is expected to be taped out before Christmas. Wu said by February next year he expects applications to be up and running on a 15 teraflops of performance (ToP) chip under 5 watts.
A second chip, designed to run under a watt, is due in mid-2018, he added.
NovuMind's new chip will support Tensorflow, caffe and torch models natively.
The endgame of Wu’s AI chip is to enable a tiny Internet-connected “edge” device to not only “see” but “think” (and recognize what it sees), without hogging the bandwidth going back to the data center. Wu calls it the Intelligent Internet of Things (I²oT).
For Wu, who hasn’t sought much publicity in the last few years, NovuMind presents, in a way, an opportunity for redemption.
Two years ago, Wu was let go by Baidu, after the Chinese search giant was disqualified from the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015. Wu subsequently denied wrongdoing in what was then labeled as “Machine learning’s first cheating scandal.”
Speaking with EE Times, he declined to discuss that event, other than noting, “I think I was set up.”
In today’s hotly pursued market of deep-learning accelerators for edge devices, NovuMind is forging ahead. After raising $15.2 million in series A funding in December 2016, NovuMind is about to begin a second round of fundraising, said Wu. “That’s why I am in Beijing now,” he told me during a phone interview.
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