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{调取该文章的TAG关键词}|Deep Learning Set to Drive Computer Industry in Next 20 Years: Alphabet Chair( 二 )


“Era of Dark Silicon” and Domain-Specific Co-Design Hennessey’s 30-minute speech also highlighted recent breakthroughs in deep learning and machine learning, including image recognition used for self-driving cars and medical diagnosis, protein folding and natural language translation. He gave the reasons why artificial intelligence (AI) suddenly made significant progress in the past few years although the concept has existed for about 60 years.
He attributed the big leaps to two major developments: the availability of massive data and massive computational capability.
“The Internet is a treasure trove of data that can be used for training. ImageNet was a critical tool for training image recognition. Today, close to 100,000 objects are on the ImageNet and more than 1,000 images per object, enough to train image recognition system really well,” he said.
The huge computational capability is mainly derived from large data centers and cloud-based computing, he said, adding that training takes hours and hours using thousands of specialized processors. “We simply didn't have this capability earlier. So that was crucial to solving the training problem.”
However, the computing capability cannot increase at a fast speed forever. It was predicted by Gordon Moore in 1975 that semiconductor density would continue to grow quickly and double every two years. But a diverge from that predicted course started in 2000, he noted in the speech.
“We are in the era of dark silicon where multicores often slow down or shut off to prevent overheating and that overheating comes from power consumption,” he said.
Hennessy proposed a new solution to the challenge. It may go in three directions. The first direction is software centric mechanisms that focus on improving the efficiency of software; the second is the hardware-centric approach and the third is the combination of the two.
“This approach is called domain specific architectures or domain specific accelerator. The idea is to just do a few tasks but do those tasks extremely well. We've already seen examples of this in graphics for example or modem that's inside your cell phone. Those are special purpose architectures that use intensive computational techniques,” he said.
“The good news here is that deep learning is a broadly applicable technology. It's the new programming model, programming with data, rather than writing massive amounts of highly specialized code. Use data to train deep learning model to detect that kind of specialized circumstance in the data,” Hennessy explained, implying that the wide applicability of deep learning has the potential to circumvent the problems of power efficiency and transistor growth.
Hennessy has been chair of Alphabet Inc. since February 2018. Prior to that, he was an independent director at Google and Alphabet from 2007. He was Stanford University’s tenth president from 2002 until his retirement in 2016, leading the university to become more academically competitive in a rivalry with Ivy League schools including Harvard and Yale. He joined Stanford’s faculty in 1977 as an assistant professor of electrical engineering.
He is also a laureate of the 2017 ACM A.M. Turing Award, along with retired UC Berkley professor David A. Patterson.
【{调取该文章的TAG关键词}|Deep Learning Set to Drive Computer Industry in Next 20 Years: Alphabet Chair】In addition to his outstanding academic and leadership achievements, Hennessy co-founded chip design startup Mips Computer Systems, which was acquired by Silicon Graphics International in 1992.

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