Challenges and Opportunities for Future Semiconductor Products in the IOT - PowerPoint Presentation

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Challenges and Opportunities for Future Semiconductor Products in the IOT Era

Cheng-Wen Wu (吳誠文)09/29/2018

AI Chips Arms RaceSource: www.jmyang.com/blog/2018/3/27/ai-chip-startup-monitor-2018-03-28

Nvidia and Google are currently leading the raceFollowed by Intel, IBM, Qualcomm, NXP, AMD, Samsung, Xilinx, HiSilicon, MediaTek, Bitmain, …And even Microsoft, Amazon, Apple, Facebook, Baidu, Alibaba, Tesla

…

Dozens of

startups

 are jumping in—what a tsunami!

Level 4 Autonomous Vehicle LeadersGoogle (

Waymo)Level 4 pilot services in Phoenix area, with the best AV technology so farNeeds mass production (partners) of the fleet of cars/trucksPartners: Fiat Chrysler, Jaguar, Walmart, Avis, AutoNation, …GM (Cruise)Acquired self-driving car start-up, Cruise Automation, in 2016To build fully autonomous vehicles in a mass-production assembly plant—for ride-sharing firstNeeds cloud/AI/5G technologiesPartners: Softbank, Lyft, …

WaymoWaymo have been conducting road tests of their self-driving cars and trucks in California and Arizona (Level 4)

They’re headed to Atlanta, Georgia, one of the biggest logistics hubs in the US [announced in March, 2018]Their self-driving cars will serve as a link to public transit in Phoenix [announced 7/31/2018]Source: Waymo & Uber, 2018Google: in 10.5 years

9M+

Miles of Road Test

25K miles/day

6B+

Miles of Simulation

Uber

:

end of July, 2018

“We will shut down our self-driving truck project (Otto)”

Law suit by Alphabet

soon after Uber acquired Otto

Pulled its

robo

-cars from the roads after fatal accident

Baidu Create ‒ Intel Inside Baidu Create 2018 in Beijing is looking like Google I/O in Silicon Valley

Intel/Mobileye announced that the Responsibility Sensitive Safety (RSS) model will be designed into Baidu’s open-source (Android like) Project Apollo and commercial Apollo Drive programsApollo platform has signed more than 100 car OEMs and tier onesThe RSS model has two separate systems:AI based on reinforcement learning proposes the AV’s next actionSafety layer based on a formal deterministic system can override an “unsafe” AV decisionBaidu was the 1st to announce the adoption of RSSMay also adopt Mobileye’s Surround Computer Vision Kit as its visual perception solutionBaidu’s PaddlePaddle (DL framework) is optimized for Intel XeonOn the shopping list: Intel’s Xeye (AI camera powered by Intel’s Movidius vision processing unit), Intel’s FPGAs, etc.

Source: EE Times, July 4, 2018

Alexa, Google Assistant, Siri, etc., have become our digital helpersThey set timers, play music, check weather, read news, schedule appointments, teach

kids how to do homework, control your home appliances, …Soon to come with display and other sensorsSmart Speakers in the USSource: www.pcmag.com/article/357520/the-best-smart-speakers, 2/15/2018

Source: Tiffany Trader, HPC Wire, 4/11/2018

US machine Summit recaptures supercomputing crown [IEEE Spectrum, 6/25/2018]

Source:

Political Calculations, 8/31/2018

網路泡沫

金融海嘯

Source: Thomas Piketty, Emmanuel

Saez, and Gabriel Zucman, Distributional National Accounts: Methods and Estimates for the United States, Sept. 2017

Source: TIME, May 28, 2018

Our Broken Economy

Source: The New York Times, Aug. 7, 2017

Global Shortage of Energy Supply

In 2014, cloud data centers consumed about 1.62% of global energyAbout 1.8% in the US (70B KWH) [US Data Center Energy Usage Report, LBNL, 6/2016]In 2017, the number was higher than 3%---can rise to 20%

by 2025

[

data-economy.com,

12/2017]

CNBC

interviewed

David Patterson

[www.cnbc.com, 5/6/2017]

“Four years ago, Google worried that if every Android user had 3 minutes of conversation translated a day using machine learning, they'd have to double their data centers.”

Alphabet spend about $10B each year on

Google

data center equipment

New data centers

or

improved equipment

Google

TPU

outperforms CPU by

15-30

X

30-80

X in energy efficiency

Speech recognition error dropped from 22% (201

3) to 4.9% (2017)Image recognition error (ImageNet contest) dropped from 26% (2011) to 3.5% (2016)Key success factors:Effective training dataScalable DNN modelEfficient hardwareTensorFlow: Open source ML platformCloud ML: ML on any dataset, of any sizeCloud AutoMLBuilt on Google’s transfer learning and neural architecture search technologies (among others)TPU usage fee: $6.50/hr

Google: DNN

Is Proven Effective

Source:

Google, 2017, 2018

Competition in AI Cloud Services

Amazon, Google, and Microsoft all want to dominate cloud AI servicesFace recognition in photos and spoken language translation have been offered by AWS, Google Cloud, and Azure Now offering AI-based platforms to almost any type of company, regardless of its size and technical sophisticationOther companies like Apple, IBM, Oracle, Salesforce, SAP, Alibaba and Baidu also have massive computing resources and talents required to build this AI utilityAI could dramatically multiply the size of the cloud marketWinners will control the OS of the future and become the most powerful companies in historySource: Peter Burrows, MIT Technology Review, Mar. 22, 2018

Source:

tefficient.com, THE SECRET BEHIND ELISA’S FINANCIALS, 2/4/2018

In 2008, DARPA issued a

challenge to researchers:Create a sophisticated, shoebox-size system that incorporates billions of transistors, weighs about 3 lbs, and requires a fraction of the energy needed by current computers—which migrates from recognition to perceptionBasically, a brain in a boxThe challenge triggered neuromorphic computing researchIBM TrueNorth ComputerIntel Loihi Test ChipIntel Neuromorphic Research Community (INRC)Neuromorphic Computing Challenge

Source: 1) HP Enterprise Labs, www.labs.hpe.com/next-next/brain

2) www.research.ibm.com/articles/brain-chip.shtml

3) newsroom.intel.com, Mar. 1, 2018

Open Issues of

Neuromorphic Computing:Basic building blocks and general architecturesConfiguring a neuromorphic device/tissueTraining (programming) a neuromorphic computerSystem softwareAppropriate applicationsComputing hardware development

Federal Vision for Future Computing[

White paper by DOE, NSF, DOD, NIST, IC (July 29, 2016)]Emerging computing architecture platforms, neuromorphic, quantum, etc.

Significantly enhancing performance while reducing energy consumption by

over 6 orders

of magnitude (from

MWatts

to Watts)

Intelligent big data sensor: autonomous and reprogrammable

Machine intelligence for scientific discovery

Cybersecurity

PCAST Report to the US President

Source: Report to the President: Ensuring Long-Term U.S. Leadership in Semiconductors, the President’s Council of Advisors on Science and Technology (PCAST), White House, Jan. 2017Semiconductors are essential to modern life

Creating new businesses and industries

Bringing massive benefits to American workers and consumers

Cutting-edge semiconductor technology is critical to US national security

The US should

Establish policies aimed at 1) developing and attracting talent, 2) funding basic research and development, 3) reforming corporate tax laws, and 4) reforming permitting practices

Establish a series of

moonshots

that would deliver

radical semiconductor advances

of much broader applicability, driving transformative innovation

Electronics Resurgence

Initiative (ERI)DARPA’s just earmarked $1.5B to fund HW projects through ERI that was announced in June 2017Goal: semiconductor innovation and circuit design upgrade in the USSome programs under the ERI umbrella:Intelligent Design of Electronic Assets (IDEA)EDA tools that learn, and are super productivePosh Open Source Hardware (POSH)Open source design and verification ecosystem for complicated SoCsDomain-Specific SoC (DSSoC)Software Defined Hardware (SDH)Three Dimensional Monolithic SoC (3DSoC)Foundations Required for Novel Compute (FRANC)Near Zero Power RF and Sensor Operations (N-ZERO)Partners: Major universities and companies

Source: DARPA 1

st

ERI

Summit, July 23-25, 2018, San Francisco

半導體產業的關鍵角色半導體

競爭有如核武競爭核武一：5G通訊晶片基頻：高通（1級），Intel（1級），三星（2），聯發科（2.5），海思（2.5），展訊（3）

射頻前端：

博通

、

Infineon

、

Kyocera

、

Murata

村田、

Qorvo

、高通、

Skyworks、 Taiyo Yuden 太陽誘

電

今年初

如果

博通買了高通，

Intel

準備出手買博通

核武二

：

AI

晶片

傳統

IC

公司：

Nvidia

（

1

）

，

Intel

（

1

）

，

IBM

（

1.5

）

，

Qualcomm

(NXP)

（

1.5

）

，

NXP

（

1.5

）

，

AMD

（

1.5

）

，三星

（

2

）

，海

思

（

2.5

）

，聯

發科

（

3

）

等

系統與雲端公司：

Google, Apple, Microsoft

, Tesla, Amazon, F

B

,

百度

,

阿

里巴巴等

核武三：車用

晶片

NXP, Infineon,

Renesas

, STM, TI, Bosch, ON

Semi, Microchip/Atmel, Toshiba,

Rohm

等

技術

（經濟）才

是

台灣

保

命的關鍵！

技術來自人才與研究

重

技術才會有人才，有人才才會有

投資

，有

未來