End of Moore's Law?

A/Prof Jeffrey Funk
Division of Engineering and Technology Management
National University of Singapore
For information on other technologies, see http://www.slideshare.net/Funk98/presentations
Implications for Smart Phones, Big Data and Software.
What is the future of work?

 Improvements in microprocessors (Moore’s
Law) are probably slowing
 But not improvements in other components,
partly because they lag microprocessors
• Graphic processors and 3D camera chips
• Wireless chips and Data Centers
 What does this mean for future of Information
Technology….. and Businesses?
• Smart phone becomes dominant device
• Internet of Things provides new data
• Most IT processing (i.e., big data) and storage moves to
cloud

 More types of data will be collected and
analyzed
 New sources of data
• “Things,” thus Internet of Things in Session 5
• Bio-sensors for health care data in Session 6
 New forms of smart phones and wearable
computing can manage data
• Better touch displays, voice recognition, Session 7
• Virtual and augmented reality, wearable, Session 8
 What opportunities will be created?
• What types of data will become important to your
business?

Session Technology
1 Objectives and overview of course
2 How/when do new technologies become economically feasible?
3 Two types of improvements: 1) Creating materials that better
exploit physical phenomena; 2) Geometrical scaling
4 Semiconductors, ICs, electronic systems
5 Sensors, MEMS and the Internet of Things
6 Bio-electronics, Health Care, DNA Sequencers
7 Displays, including touch displays
8 Voice and gesture interfaces, AR, VR, wearables, neural
9 Information Technology and Land Transportation
10 Smart Cities: lighting, food, water, 3D printing
This is Fourth Session of MT5009

 What do numbers say about Moore’s Law?
• Microprocessors and Flash memory
• Wireless chips, Data Centers
 What does this mean for:
• Smart phones?
• Software and Big Data?
 Alternatives to Silicon and von Neumann
• Organic transistors
• Carbon nanotubes, Graphene
• Atomic transistors
• Synapse, AHaH
• Quantum computers

Is Moore’s Law Ending?
Economist, March 12-18, 2016

People Have Been Talking About These Problems for
Many Years; Is this a Real End for Moore’s Law?

 Becoming increasingly expensive
 Without further reductions, hard to
• increase the number of transistors per chip
• reduce cost per transistor
 Main problem is photolithography
• See below
If photolithography and other problems
can’t be solved,
• New solutions are needed (see below)

Source: Chuck Moore, Data Processing in Exascale-Class Systems, April 27, 2011. Salishan Conference on High Speed Computing
Power and Heat Problems Led to Multiple Cores and
Prevent Further Improvements in Speed

 Can the number continue to be increased?
 Many say NO
 Hard to break up problems into still
smaller ones for general purpose
processors
 Additional problem is whether costs have
stopped falling
• Related to problems with reducing feature sizes
and increasing number of cores?

http://www.economist.com/blogs/economist-explains/2015/04/economist-explains-17
Intel Says Differently
(1 Feb 2016)
Intel reiterated its claim it
has reduced cost per
transistor at its 22 and
14nm nodes at a rate
slightly better than the
industry’s 30% historical
trend. That’s despite the
fact the cost of developing
each new process has
risen to 30% in the last
few nodes up from a
historical trend of 10%.
http://www.eetimes.com/document.asp?doc
_id=1328835
If Costs are no Longer Falling,
There is Big Problem

Intel Says Costs Continue to Fall

http://www.alixpartners.com/en/Publications/AllArticles/tabid/635/articleType/ArticleView/articleId/941/Cashing-in-with-Chips.aspx#sthash.QDcy503U.dpbs
Some Costs Rise as Feature Sizes Become Smaller

http://www.chipdesignmag.com/bursky/
Cost of Photolithography (Litho) is Rising
Faster than other Process Equipment

Photolithography Used to Form Patterns in Layers
Width of this “line”
is one type of
feature size.
Another is thickness
Note: Masks are made with
Electron Beam,
which is even more
expensive

Bottleneck in photolithographic process is wavelength of light.
Feature sizes are now smaller than wavelength of visible light
Source: http://www.soccentral.com
/results.asp?CatID=488&EntryID=30894

 Must compensate with strong optical
lenses and error correction software

Basically putting a supercomputer in a
photolithographic equipment

This is one reason for rising cost of
fabrication facilities

Light is emitted
by a plasma
Need
1) Vacuum since
air absorbs
small wave-
length light
2) stronger light
source to
speed up
processing
Raises costs
Need fast
processing to
justify high costs
Recent Development of
Extreme Ultraviolet

Latest
EUV
lithography
system
achieves
28 wafers
per hour
but needs 200
wafers per hour
to be
economical
Intel says it
will use
EUV for
7nm
Source:
http://nextbigfuture.com
/2014/06/extreme-
ultraviolet-lithography
-hopes.html#more

http://ieeexplore.ieee.org/ieee_pilot/articles/96jproc11/jproc-MSanvido-2004319/article.html
Memory Also Depends on Smaller Feature Sizes
And thus Faces Same Problems as Microprocessors do
nm

http://www.theregister.co.uk/2012/10/12/nand_shrink_trap/
Additional Problem for Flash Memory:
Number of electrons available to hold the
binary data in each cell decreases

 It can be cheaper to
add more layers than
to reduce feature sizes
This is particularly true
for memory chips,
which are
architecturally simple
 Samsung is moving
the fastest in memory
 Other firms and chips
are expected to follow

Continued Increases in
Flash Memory Size

Continued Reductions
in Flash Memory Cost
Reductions in Feature Size Continue to Proceed Over time

TSV: through silicon via
TSV: Through Silicon Via
Can Combine Different Types of
Designs on a Single 3D Chip

In addition to problem of electron numbers,
Flash Memory has Slow Read Write Speeds
http://isscc.org/doc/2013/2016_Trends.pdf
(Non-
Volatile

http://isscc.org/doc/2014/2016_Trends.pdf
But Flash Continues to Win

 New form of non-volatile memory
 1,000 times faster than NAND flash
memory
 10 times more data stored than in DRAM
 Unique way to store data, using vertical
columns of circuitry linked by crisscross
grid of microscopic wires
 Companies will initially offer two-layer
chips that have 128 Gb, matching some
NAND chips
http://www.micron.com/about/innovations/3d-xpoint-technology

 They can replace flash memory, SRAM, and
DRAM and thus enable new and better
architectures
 Most electronic products use all three
• SRAM (fastest and most expensive volatile memory) and
DRAM (slower and cheaper) store data for
microprocessors
• Flash memory: non-volatile memory
 Combining them on single chip
• can reduce overall access and processing times
• can eliminate bottleneck that currently exists between
memory and processor chips (see below)

 What do numbers say about Moore’s Law?
• Microprocessors and flash Memory
• Smart phones and Biometrics?
• Big Data, Internet of Things?
• Synapse, AHaH

May 7, 2016
http://www.wsj.com/articles/nvidia-pushes-chip-speed-higher-price-lower-1462594938

Launch Year 2010 (GTX 580)
2014 (GTX Titan
Black)
2016 (GTX Titan X) Pascal 2017
GPU Process 40nm 28nm 28nm
16nm (TSMC
FinFET)
Flagship Chip GF110 GK210 GM200 GP100
GPU Design
SM (Streaming
Multiprocessor)
SMX (Streaming
Multiprocessor)
SMM (Streaming
Multiprocessor
Maxwell)
SMP (Streaming
Multiprocessor
Pascal)
Maximum
Transistors
3.00 Billion 7.08 Billion 8.00 Billion 15.3 Billion
Maximum Die Size 520mm2 561mm2 601mm2 610mm2
Stream Processors
Per Compute Unit
32 SPs 192 SPs 128 SPs 64 SPs
Maximum CUDA
Cores
512 CCs (16 CUs) 2880 CCs (15 CUs) 3072 CCs (24 CUs) 3840 CCs (60 CUs)
FP32 Compute
1.33
TFLOPs(Tesla)
5.10 TFLOPs
(Tesla)
6.10 TFLOPs
(Tesla)
~12 TFLOPs
(Tesla)
FP64 Compute
0.66 TFLOPs
(Tesla)
1.43 TFLOPs
(Tesla)
0.20 TFLOPs
(Tesla)
5.5 TFLOPs(Tesla)
Maximum VRAM 1.5 GB GDDR5 6 GB GDDR5 12 GB GDDR5 16 / 32 GB HBM2
Maximum
Bandwidth
192 GB/s 336 GB/s 336 GB/s 1 TB/s
Maximum TDP 244W 250W 250W 300W

 Feature sizes lag those on micro-processors
by about 5 years
 Easier to break down graphics processing
into smaller problems and thus use multiple
cores
• >3,500 cores on GPU
• About 20 on general purpose microprocessor
 Future of GPUs is machine learning
 Nvidia, AMD, and new entrants are pursuing
this market
• New entrants such as Movidius and Nervana offer
special-purpose processors for machine learning

 Use GPUs to
• analyze medical images, spot anomalies on CT scans
• study photos, audio files social media posts
 Blue River Technology, Nervana customer
• analyzes crop and weed photos to determine where to
spray (5,000 decisions a minute)
 Market growth between 2015 and 2024
• For GPUs: from $43.6 million to $4.1 billion
• Software spending by enterprises: from $109 million to
$10.4 billion
http://www.wsj.com/articles/new-chips-propel-machine-learning-1463957238

PixelSize(µm2) Resolution of Camera Chips
Continues to Increase, Far from Limits
International Solid State Circuit State Conference, Technology Trends, 2016 http://www.future-fab.com/documents.asp?d_ID=4926
Microprocessor
feature
sizes are less than
20 nm

 Cost of 3D sensors falling quickly
 Intel’s Real Sense in more than 25 models of
laptops and will be in Android phones by 2017
 RealSense gives 3D vision via a four-
millimeter-thick strip that includes two
cameras and one processor
• By comparison, Kinect required a foot-long box that
relied on Xbox’s processors
 Other sources claim that cost of 3D vision has
dropped from $200 to $20
http://www.wsj.com/articles/more-devices-gain-3-d-vision-1444859629

 Intel released
• F200 in 2015
• SR300 in 2016
 Both create high quality 3D depth video stream
 SR300 adds IR laser projector, fast VGA, shorter
exposure time, dynamic motion up to 2m/second
 Applications
• Gesture interfaces, augmented reality
• Robotics, 3D scanning
• Driverless vehicles, drones
https://software.intel.com/en-us/articles/a-comparison-of-intel-realsensetm-front-facing-camera-sr300-and-f200

 Reductions in feature sizes lag
microprocessors by many years
 Thus are slower, and have fewer cores, partly
because lower power consumption is required
 Parallel data transfers being implemented for
cellular and WiFi, just like multi-cores of GPUs
 In future,WiFi will dominate smart phones,
with most processing in the cloud
• More bio- and environmental sensors
• More image sensors for 3D reconstruction
• More data mining of user behavior

Speed
of PC
Micro-
processors
W

Improvements are Still Occurring
Along Many Dimensions
Source: International Technology Roadmap for Semiconductors

Other Sources are also Optimistic
(International Solid State Circuits Conference)

 LiFi = Light Fidelity
 LiFi will become economical in near future
as LEDs become cheaper (see Session 10)
 LiFi enables much faster speeds
• Twenty times!
 Much lower power consumption
• 100 times!
 Previously required line of sight
 But new forms of LiFi can handle reflections

 What do the numbers say about Moore’s Law?
• Microprocessors and Flash memory
• Smart phones?
• Software and Big Data?
• Synapse, AHaH

 Everything is going
to cloud
 Data centers are
more efficient than
stand-alone
computers
 Computers operate
24/7 in data centers
 New architectures
and new software are
making them even
more efficient

 Virtualization
• split computers into virtual
machines, each with OS and
programs
• VM ware has been largest success
• Enables higher work loads
Other methods such as
• Containerization
• Orchestration
http://www.cisco.com/c/en/us/solutions/collateral/service-
provider/global-cloud-index-gci/Cloud_Index_White_Paper.html
progress without profits, economist, sept 19, 2015. P. 61

 New architectures and software enable faster
analysis of unstructured data
 GFLOPS (giga-floating operations per unit)/Watt
• 0.4 in 2010 to 67.8 in 2021
 Bandwidth
• 1 Gb/second in 2010 to 100 Gb/sec in 2021
 In combination with smart phones and WiFi, these
rapid improvements will enable
• many new types of applications
• automation of many functions
• Machine Learning
Source: International Technology Roadmap for Semiconductors

 IBM has made quantum computing
available on the cloud (2016) http://flip.it/chi2D
 This has much higher speeds, for some
applications
 Speeds continue to rise and costs continue
to fall
Will quantum computing dominate cloud
computing in 10 years?
 See http://www.slideshare.net/Funk98/superconductivity-15131282 and a few
slides at the end of tonight

The Smart Phone Will be Used
Much More than PCs

Internet Advertising has Moved to the Internet

And Will Now Move to the Smart Phone

Though Data is Still Expensive for Poor Countries
it will get cheap, let’s look at how this occurred
and will continue to occur

 How did they become economically
feasible?
• What are main costs for smart phones?
• What determines their performance?
• What levels of performance and cost were needed in
the microprocessors and memory for smart phones
to become feasible?
• When did this happen?
 What happens as the components become
better and cheaper?
• What types of high performance phones?
• What types of low-cost phones?

Type of
Product
Final Assembly Standard Components1
Number of
Data Points
Average
(%)
Number of
Data Points
Lower Estimate for
Average2 (%)
Smart Phones 28 4.2% 26, 28 76%, 79%
Tablet
Computers
33 3.1% 33, 33 81%, 84%
eBook Readers 6 4.9% 6, 9 88%, 88%
Game Consoles 2 2.4% 2, 2 64%, 70%
MP3 Players 2 3.4% 2, 9 74%, 75%
Large Screen
Televisions
2 2.4% 2, 2 82%, 84%
Internet TVs 2 5.7% 2, 2 57%, 61%
Google Glass 1 2.7% 1, 1 62%, 64%
Cost Breakdown for Electronic Products
1 Values as a percent of total and material costs
2 Excludes mechanical components, printed circuit boards, and passive components

Type of
Product
# of
Data
Point
Mem
ory
Micro-
Proc-
essor
Displ
ay
Camer
a
Connecti
vity,
Sensors
Bat-
tery
Power
Mgmt
Phones 23 15% 22% 22% 8.2% 7.9% 2.3% 3.8%
Tablets 33 17% 6.6% 38% 2.9% 6.3% 7.3% 2.5%
eBook
Readers
9 10% 8.1% 42% .30% 8.3% 8.3% Not
available
Game
Console
2 38% 39% none none Not
available
none 5.8%
MP3
Players
9 53% 9% 6% none Not
available
4% 3.5%
TVs 2 7% 4.0% 76% none Not avail. none 3.0%
Internet
TVs
2 16% 31% none none 10.5% none 3.5%
Google
Glass
1 17% 18% 3.8% 7.2% 14% 1.5% 4.5%
Contribution of “Standard Components” to Costs
of Selected Electronic Products

Measure iPhone iPhone 3G iPhone 4 iPhone 5 iPhone 6
Operating
System
1.0 2.0 4.0 6.0 8.0
Flash
Memory
4, 8, 16GB 8 or 16GB 8, 16, 64GB 16, 32, 64GB 16, 64, or 128GB
DRAM 128MB 128MB 512MB 1GB 1GB
Application
Processor
620MHz Samsung 32-bit RISC 1 GHz dual-
core Apple A5
1.3 GHz dual-core
Apple A6
1.4 GHz dual-core
Apple A8
Graphics
Processor
PowerVR MBX Lite 38 (103
MHz)
PowerVR
SGX535 (200
MHz)
PowerVR
SGX543MP3 (tri-
core, 266 MHz)
PowerVR GX6450
(quad-core)
Cellular
Processor
GSM/GPRS/
EDGE
Previous plus
UMTS/HSDPA
3.6Mbps
Previous plus
HSUPA
5.76Mbps
Previous plus LTE,
HSPA+, DC-HSDPA,
4.4Mbps
Previous plus LTE-
Advanced, 14.4Mbps
Display
resolution
163 ppi (pixels per inch) 326 ppi 401 ppi
Camera
resolution
Video speed
2 MP (mega-pixels) 5 MP
30 fps, 480p
8 MP
30 fps at 1080p
8 MP
60 fps at 1080p
WiFi 802.11 b/g 802.11 b/g/n 802.11 a/b/g/n 802.11 a/b/g/n/ac
Other Bluetooth 2.0 GPS,
compass,
Bluetooth
2.1,
gyroscope
GPS, compass, Blue-
tooth 4.0, gyroscope,
voice recognition
Previous plus finger-
print scanner, near-
field communication
Evolution of iPhone in Terms of Measures of Performance
Fps: frames per second
480p: progressive scan of 480 vertical lines

 What Levels of Performance and cost
were needed in each Component?
• Memory
• Microprocessors
• displays

 760 songs, 4000 pictures (4 megapixel
JPEG), four hours of video, or 100
apps/games, or some combination
 Equal usage
• 190 songs
• 1000 pictures
• one hour of video
• 25 apps/games
 Was this necessary, would 1GB have been
sufficient?

The Average User Downloaded 58 Apps or a Significant
Fraction of Memory Available in 4GB Phone

 Cost of iPhone 5 varies from $207 to $238
depending on flash memory capacity
• 16GB, 32GB, or 64GB
 For iPhone 4s, costs range from $196 to $254 for
same range in flash memory
 For iPhone 3GS, 16GB of flash memory are $24
thus suggesting costs for same change in capacity
would range from $179 to $251
 In percentage terms, same changes in flash
memory capacity led to increase of 40% in iPhone
3GS and increase of only 15% in iPhone 5

 Needed sufficient processor to have 3G
network capability
 Needed sufficiently inexpensive
processor
 What about camera,WiFi, gyroscope,
other sensors?

 What components are experiencing rapid
improvements?
 Can they tell us something about “next big thing”
 Improvements will probably continue in
• Microprocessor, memory and other ICs
• MEMS, bio-electronic ICs
• Displays including flexible ones
• Lasers, LEDs, photo-sensors, and other sensors
• Speeds of cellular networks and WiFi
• New forms of user interfaces (gesture, touch)
 Open source software is becoming more
available

 New features, perhaps for high-end phones
• Health care: phones monitor health (heart rate, brain
wave, blood pressure) using sensors
• Home automation: use phones to control homes
• Voice-activated assistant for unfree hands (e.g., drivers)
• Engineering assistant: environmental data (temperature,
pressure, air and water quality)
 Different phones for different applications?
• Or one phone does everything?
• Specific phones must be defined for specific users

 What are entrepreneurial
opportunities?

Will Apple be
Disrupted?
Apple has
highest prices
Does it Deserve
High Prices?
Why might
Apple be
disrupted?

Type of
Product
# of
Data
Point
Mem
ory
Micro-
Proc-
essor
Dis
play
Cam-
era
Connect-
ivity,
Sensors
Bat-
tery
Power
Mgmt
Phones 23 15% 22% 22% 8.2% 7.9% 2.3% 3.8%
Includes
WiFi
Contribution of “Standard Components” to Phone Costs
Can any of these components be eliminated to create a much
cheaper phone (and perhaps a much cheaper phone service?
Are there improvements in components and/or
technological trends that can help us think
about components to eliminate?
How about a Low-End Phone: what might emerge?

 Can microprocessors and memory be eliminated
to create low-end phones that bypass network
providers (SingTel, StarHub)
• Lower cost phones
• Lower cost services
 If WiFi is main connection and it works good
enough
• Can we reduce memory capacity?
• Can we reduced performance of application processor?
 Lower resolution cameras, displays, and other
components will also reduce costs
 How might open source software enable lower
costs?

 What are the entrepreneurial
opportunities?

 Concept of service
• Combine WiFi routers into integrated services
• Access cellular network when WiFi isn’t available
• How long will cellular service providers continue selling network
space to new entrants?
 In U.S., Republic Wireless, Scratch Wireless,
FreedomPop, Google, soon cable companies. In France,
service called “Free”
 But Korea may be leader – large use of WiFi, great phones
from Samsung, and great mobile content and services
 In India, Uber will Offer Free Wi-Fi in taxis, Google also
(http://blogs.wsj.com/digits/2016/01/22/google-brings-wi-fi-to-mumbais-railway-station/?mod=ST1)
http://www.wsj.com/articles/google-unveils-wireless-service-called-project-fi-1429725928; http://nyti.ms/1AFMiFW; http://nyti.ms/1HI2BkW;
http://www.economist.com/news/business/21654602-wi-fi-first-technology-will-be-great-consumers-disruptive-mobile-firms-change
http://www.wsj.com/articles/uber-to-offer-india-passengers-free-wi-fi-1440136803

 Messaging Apps are
most widely used
function on most
smart phones
 Some used for more
than messaging
• Particularly in China
• For payments, hotel,
rides sharing, food
delivery, restaurant
 Voice recognition is
future (Session 7)
http://www.wsj.com/articles/the-future-of-texting-e-commerce-1451951064
http://www.economist.com/news/business-and-finance/21696477-market-apps-maturing-now-one-text-based-services-or-chatbots-looks-poised

http://www.wsj.com/articles/global-telecoms-struggle-to-answer-challenge-from-
messaging-apps-1464038370

 $40 billion in apps downloaded in 2015
(100 billion apps available)
 20 most successful developers grab nearly
half of revenues
 For using apps to order things,
• services must understand chat, thus AI is required
 Interact with bank accounts, get news, do
bookings, find sports shoes, respond to
health care questions, interact with doctors

 Hard to switch between messaging apps
• Inconvenient for many users
• Hard to move data between them
 Similar to incompatible word processing,
spreadsheet and power point software in 1980s
 Microsoft Windows integrated them in late
1980s
 Whose apps will become the ‘hub’ for many
services? FB, Uber, or somebody else?
 What will be roles for AI and bots?

Better Apps Support Growth in Demand Economy

1. Finger print
2. Palm veins
3. DNA
4. Iris recognition
5. Facial recognition
6. Voice recognition
7. Signature recognition
8. Palm print
9. Hand geometry
10.Retina scan
11.Ordure/Scent

 Google will begin testing an alternative
to passwords next month
• With a goal to eliminate complicated logins for
 Google introduced new feature to
developers at company’s I/O conference
in May 2016
 Called the Trust API
 To be initially tested with several very
large financial institutions
http://flip.it/gPoep

89
Biometric
Technology
Accuracy Cost Device Required Social
Acceptability
DNA High High Test Equipment Low
Iris recognition High High Camera Medium -Low
Retina scan High High Camera Low
Facial recognition Medium -
Low
Medium Camera High
Voice recognition Medium Medium Microphone,
telephone
High
Hand geometry Medium -
Low
Low Scanner High
Finger print High Medium Scanner Medium
Signature
recognition
Low Medium Optic pen,
touch panel
High
http://kaitleencrowe.com/2015/01/22/bi
• Compared with other Bio Technologies, Finger Print is the best choice

How Fingerprint are acquired?
Source: http://360biometrics.com/faq/fingerprint_scanners.php
Optical Sensor
Ultrasonics with RF
Sensor
Optical Sensor
module
Capacitive Sensor
Thermal Sensor

91
Fingerprint Sensors Comparison
Optical Capacitive Ultrasound
Size Relatively big and
require camera
Can embed into
small devices
Can embed into
small devices
Method Image capture RF Field RF Field
Cost Middle Low High
Accuracy May be affected by
dirt or water
May be affected by
dirt or water
Will not be
affected by dirt or
water
Working Current 120 mA 200 mA 6 µA
Source: http://yourbusiness.azcentral.com/comparison-fingerprint-scanners-27754.html
http://artofcircuits.com/product/optical-fingerprint-sensor-module-fpm10a
http://www.techshinobiometrics.com/products/fingerprint-identification-products/fingerprint-oem-modules/
http://www.sonavation.com/ultrasound-biometric-sensor

 The most commonly used are:
1. Minutiae matching (commonly used)
2. Pattern matching
http://www.biometric-solutions.com/solutions/index.php?story=fingerprint_recognition
http://biometrics.mainguet.org/types/fingerprint/fingerprint_algo.htm

93http://biometrics.mainguet.org/types/fingerprint/fingerprint_sensors_manufacture.htm
Average Selling Price of Fingerprint
Sensors are Dropping

94
https://www.tractica.com/newsroom/press-releases/fingerprint-readers-in-mobile-devices-to-surpass-1-billion-unit-shipments-annually-by-2021/
Demand for Finger Print Sensor is Growing

 Mobile phones
Mobile phone banking, e-commerce,
stock trading
Banking
Cars
Houses
 thumb drives
Offices

97
Applications for Biometrics
Payment
$$$
Public Transport Convenient Stores
Vending
Machines
Computers

Routine Non-Routine
Manual Assembly line:
being automated
Nail Salon; won’t be
automated
Cognitive Many Jobs are
being Auto-
mated!!!!
Hardest to Automate.
Where you want to be
Easier to make computers exhibit adult (calculations)
than child (perception and mobility) behavior
Low-level sensorimotor still require much
computational resources

 What types of software and other
automation should be used?
 What jobs should be done by humans?

 Not a question just for MIS
 A question for top management
 Want low costs, high quality, fast
response and many other things
 Let’s look at what is happening

 Apple has never been a big player in the $2
trillion annual spending on workplace
technology
 But now Apple is inviting firms to develop such
programs (working with Cisco and others)
 Encouraging providers of complementary apps
to collaborate
 20% of tablets used globally in 2018 will be
owned by businesses, up from 12% in 2014
 High expectations for retailers, restaurants, sales
presentations, government offices
http://www.wsj.com/articles/with-ipad-sales-cooling-apple-leans-on-partners-1439422814

Slack is more like
messaging apps
Focus on people or
projects, not time
More like a
conversation!
Easy to understand
internal vs. external
Easier to see most of
your conversations in
one view

 Slack allows third parties
to build apps
• Order lunch is one
• Other small tasks will be
automated
• Intelligent personal assistant
for groups?
 Will it absorb functions
from other software?
 Will similar things happen
with other enterprise
software?
• Sales software?
• Marketing software?

Zenefits Changed Human Resource Management,
Replacing Benefits Brokers

 Examples: journalism, accounting, engineering,
architecture, legal, education
 Previously face to face and customized services;
• Now mass customization and telepresence with computers
 Software automates work, transforms work
(enabling people to be more proactive), and
enables customers to bypass professionals
• Nurses do doctors work
• Paralegals do lawyers work
• Students learn without teachers
See The Future of the Professions, Daniel and Richard Susskind)

 The number of articles written by robots is
growing rapidly
 Associate Press uses Automated Insights’
Wordsmith platform to create more than 3,000
financial reports per quarter
 Kristian Hammond, Narrative Science’s co-
founder, estimates that 90 percent of news could
be algorithmically generated by the mid-2020s,
much of it without human intervention
 Just input a few facts and let the algorithm write
the paper
http://www.nytimes.com/2015/03/08/opinion/sunday/if-an-algorithm-wrote-this-how-would-you-even-know.html?rref=opinion
&module=Ribbon&version=origin&region=Header&action=click&contentCollection=Opinion&pgtype=article

 Accounting continues to become more automated,
particularly tax compliance
• Cash flow done with QuickBook, Xero, Kashflows
• Internal accounting work focuses on problem solving, like
collecting payments
 Tax work changing from compliance to planning
• Compliances done with TurboTax, H&R Block, At Home
TaxACT
• But even planning is threatened; planning and compliance
are different sides of same coin
• Compliance works forward from rules and regulations while
planning works backwards from these rules and regulations

 Continuous auditing is next step
• Samples (chosen by heuristics) used in past to
minimize calculations
• Big Data enables software to analyze 100% of the data,
and continuously
 Governments use software and big data to assess
tax returns, estimate chances of fraud
• Many require original electronic records, as opposed to
paper
• Electronic invoices are harder to fake than are paper
ones

 Better software continues to emerge, enabling
• more high level design work
• more design options can be considered
 Lower cost software also becoming available
• Enables more design options to be considered by small
firms, individuals, emerging economies
• For example, water flow analysis for fish farms
 Can you think of other examples?
• Maybe your job should be automated
• Better to provide the solution than to have your job
eliminated by someone else’s solution

 Software eliminates wooden models
• Use CAD and CAE ( VR and AR discussed in Session 8)
• Software creates more design possibilities; input objectives and
designs are proposed
• Computations carried out to test more radical designs
 Cheap forms of software are emerging
• Individuals use software to become their own architects
• Open source designs becoming widely available:
Sketchup3d has one million designs while Grab Cad has 660,000
designs. Designs shared on many sites (even Pinterest)
 Different people do different tasks
• Less need for vertical hierarchy. Use network model
• Can probably have designs checked by city governments

 Most work involves paper work
 Filling out forms, asking questions
• Much of this can be done with online
questionnaires
 Large cases involves lots of research,
which can now be done with computer
searches
 Computers and artificial intelligence will
continue to eliminate legal jobs

ABA: American Bar Association
BLS: Bureau of Labor Statistics
http://www.mybudget360.com/law-school-bubble
-law-tuition-law-degrees-in-bubble-applications-down/
Graduates

 Big move towards Pre-Fab/Modular Housing
• To reduce construction time, http://www.dirtt.net/
• No screws, nails, snap fits
• Change dimensions of one part, CAD system
automatically changes dimensions on other parts
• Uses ICE software, borrowed from video games
• Easy to reconfigure designs and rooms
 Can augmented reality software help? See
session 8

 Pre-fab Housing Method is one reason
some Chinese companies can construct
large buildings in less than one month
 Many such articles and videos but here
are two of them
 http://www.theguardian.com/world/2015/apr/30/chinese-
construction-firm-erects-57-storey-skyscraper-in-19-days
 http://www.dailymail.co.uk/news/article-2083883/Ark-Hotel-
construction-Chinese-built-30-storey-hotel-scratch-15-days.html

 Many ways to learn without schools and
teachers
 More than 10 million unique visitors each
month in 2014 for Khan Academy
 700,000 education related videos on YouTube
 750,000 educational apps installed in 2014
 70 million unique visitors to slideshare 2014
 New philosophy for education: need guide on
the side, not sage on the stage
See these slides for more details: http://www.slideshare.net/funk97/is-a-new-business-model-for-universities-needed

Even
Museums
are Talking
about
Big Data

Sales Software
They mine sales staff
emails, calendars, social-
media feeds, as well as
news articles and
customer databases
Proposes potential
customers, ranking them in
order of likelihood to buy
Can tell sales staff when
client is reading their email

 Google (and others) use data to determine
the ads that are displayed when you
• use search engine
• visit website
 Ad sites compete to provide ads each time
you click
• They propose ads and prices based on your cookies.
• Amazon and other web sites also use this data to adjust
prices depending on customer characteristics, time of
day and other things
Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence, August 4, 2015, Jerry Kaplan

 Mobile ads work differently
• InMobi offers questions based on all
your on-phone activity
• Can somebody do this better?
 What about ad blocking
software?
• Many startups provide this software
• Is there a better way to do this?
• One that can satisfy both users and
advertisers?
• How will this war end?
Block Shock, Economist, June 6, 2015, p. 52

 In one study of 140,000 test emails,
researchers at Verizon reported that 23%
of recipients opened and 11% clicked on
attachments
 Training doesn’t help
 Solution is to automate around them
• Better email filtering
• Something else?
http://www.wsj.com/articles/how-to-improve-cybersecurity-just-eliminate-the-human-factor-1453125602?mod=LS1

 Many billion dollar club startups use Big
Data
• Peer-to peer lending: Lufax, Prosper
Marketplace, Social Finance, Funding Circle,
Lending Club
• E-commerce payment: Klarna
• Other: Zhong an Online (insurance), Hanhua
Financial (credit guarantor), Credit Karma
(credit scores), Sunrun (solar leasing)
 But what is next?
 What about smart phone apps?

 As firms manage more money, aren’t
they becoming like banks?
 Klarna
• Offers simple payment system (e-mail
identifiers) for smart phone users that is easier
than inputting credit cards
• 65,000 online merchants, 45 m users
• It allows users to pay after receiving products
and Klarna assumes the risk
• Klarna judges risk based on Big Data
• Now it is extending loans (also PayPal is)
Getting more ambitious, Economist, Feb 6, 2016

 Evaluates
creditworthiness of
prospective borrowers
 Big Data analysis finds
patterns of credit
worthiness and apps
look for this
creditworthiness
 Very popular in Africa
where mobile banking
is popular http://www.wsj.com/articles/lending-startups-look-at-borrowers-
phone-usage-to-assess-creditworthiness-1448933308

 In U.S. poor people often paid by checks
• but banks charge a lot for checks when bank account
balances are small
 Governments should
• pay workers in another way
• require companies to pay in another way
 What should the other way be?
• Messaging apps?
• Other mobile phone apps?
• Security may be the biggest issue

 Individuals are coding decision rules at home
 More than 170,000 people enrolled in a popular
online course,“Computational Investing” taught at
Georgia Institute for Technology
 What types of decision rules?
 For example, if stock volumes hit minimum
threshold
• and price crosses above 200-day moving average, buy
• And price falls below 200-day moving average, sell
 http://www.wsj.com/articles/an-algo-and-a-dream-for-day-traders-1439160100

 Two big challenges
• High regulation
• High capital requirements, which bring risk
 Solutions
• Lemonade uses peer-to peer approach, like FinTech
banks
 Get friends to adopt risk, since they understand situation
of friends
• Metromile tracks mileage
 More mileage, higher the risk
 Many others (Progressive, Aviva) track braking and speed
Against the Odds, Economist, January 30, 2016, P. 59

Satellite Data - utilize image data from orbiting satellites to
measure number of cars in Walmart parking lots or farm health
based on color of crops.
Web/App/Social Media Data –mine social media or use data
firehoses from web/ mobile to understand what’s happening in
world or how people are interacting with their devices.
Weather Data –developing weather models and utilizing more
sensors to get better localized data or improve weather
forecasting.
Location/Foot Traffic Data –use different means to
understand where consumers are going by measuring foot
traffic via check-ins, video analysis, etc.
https://www.cbinsights.com/blog/alternative-data-startups-market-map-company-list/?utm_source=CB+Insights+Newsletter&utm_
campaign=5442a1353d-Top_Research_Briefs_5_21_2016&utm_medium=email&utm_term=0_9dc0513989-5442a1353d-86622821

Alternative Credit - new credit models that utilize sources
of alternative data (like mobile usage).
Credit Card Transactions – use anonymous aggregate
transaction data to understand trends in consumer
purchasing habits.
Alternative Data Monetizers/Aggregators – companies
who pay for access to individual data streams which become
more valuable in a bundle, and then sell those packages to
investors
Local Prices – what’s happening to prices and inflation by
aggregating data from ground-level sources.
https://www.cbinsights.com/blog/alternative-data-startups-market-map-company-list/?utm_source=CB+Insights+Newsletter&utm_
campaign=5442a1353d-Top_Research_Briefs_5_21_2016&utm_medium=email&utm_term=0_9dc0513989-5442a1353d-86622821

 E-commerce sites vary prices by time,
day, and location
 Trying to maximize profits through small
changes in prices
 Some of this is personalized pricing
 Some of this is time of day pricing
 Can this be applied to physical stores?
 What is next?
http://www.wsj.com/articles/now-prices-can-change-from-minute-to-minute-1450057990

Uber’s System Uber’s System
Not Working Working

• Dynamic pricing for parking
• Price changes over day and different quarter
• Drivers can check for vacant spots and price on smartphone to make
better choices
• Real-time physical location data can also help make better
decisions about parking garages

Price Display by Smartphone
In future, smart phones can replace traditional paper or
electronic screen price tag and act as price display tool
Customers obtain dynamic price as well as other
information of goods by just tapping NFC tag or scanning
the QR code

1. Smartphone design and mfg
with NFC ID identification
function
2. NFC chip manufacturing
3. Mobile Apps to read QR
code & NFC tag, combined
with price analysis function
4. Data processing platform
that deals with mass
dynamic pricing data
analysis
Future Opportunities for Price Display
by Smartphone

 Google, IBM, Facebook made their
machine-learning software available for
free under and open-source license
• Google:TensorFlow system
• IBM: SystemML
 They want their systems to be
• tested, tuned, and adapted
• built upon, improved, and extended
Open source is necessary to attract
academics
http://blogs.wsj.com/digits/2015/11/09/why-google-is-willing-to-give-away-its-latest-machine-learning-software/?mod=ST1
http://www.wsj.com/articles/ibm-turns-up-heat-under-competition-in-artificial-intelligence-
1448362800?mod=WSJ_TechWSJD_NeedToKnow

 Computers have beaten best chess and
Jeopardy players
 Computers can help doctors diagnose patients
 Computer matches medical knowledge with
patient’s symptoms, medical histories with test
results
• formulates diagnosis and treatment plan
• Doctors cant read all journals nor remember everything
they read
 Discussed more in Session 6
Sources: The Second Machine Age: Work, Progress, and Prosperity in aTime of Brilliant Technologies, Erik Brynjolfsson, Andrew McAfee
http://www.research.ibm.com/cognitive-computing/watson/watsonpaths.shtml#fbid=NAFH6hHnYVY

 Learning about my music likes, partly
through my friends likes
 Searching through my photos
• find photos that match “wedding” and “mom”

 What do the numbers say about Moore’s Law?
• Microprocessors and flash Memory
• Smart phones and Biometrics?
• Big Data, Internet of Things?
• Synapse, AHaH

 Built from organic molecules rather than silicon
 Advantages
• greater flexibility
• lower manufacturing temperature (60-120° C)
• lower-cost processes such as roll-to roll printing
 Disadvantages
• lower mobility and switching speeds compared to silicon
• usually do not operate under inversion mode
 Current Market
• Circuits for Electronic paper (e.g., e-Books),
OLEDs and other displays
 Future Market
• Greater use of organic transistors in cases where flexible
electronics are useful
• Replacement of ICs

Huanli Dong , Chengliang Wang and Wenping Hu, High Performance Organic Semiconductors for Field-Effect
Transistor, Chemical Commununications, 2010,46, 5211-5222

http://pubs.rsc.org/en/content/articlelanding/2010/cs/b909902f#!divAbstract

 Dramatically lower costs
 But also lower performance
 Other types of materials can also be
printed
• Conductive inks
• Electronic paste
 Also other applications for such
materials

Very high conductivities
In medium term, can be used in channel area
(under gate) in place of silicon for faster
transistors
In long term, can they be designed with
different properties (e.g., conductors,
insulators, semiconductors) so that transistors
can be built with them

Improvements in Purity of CNTs (and Increases in Density)
Source: Electronics: The road to carbon nanotube transistors, Aaron D. Franklin
Nature 498, 443–444 (27 June 2013)

IBM Says they are five times faster and
will be ready around 2020 when feature
lengths reach 5nm (now 14 nm)
• Built on top of silicon wafers
• Each transistor uses six nanotubes lined up in
parallel to make a single transistor
• Challenge is to make them self-assemble
Nantero has shipped samples of
nanotube based memory (NRAM)
• Produced in CMOS fabs (20 ns access times)
Source: Technology Review, http://nextbigfuture.com/2014/07/ibm-says-nanotube-transistors-chips.html#more
http://nantero.com/mission.html; http://blogs.wsj.com/digits/2015/06/02/carbon-nanotube-chips-spark-investment/
http://www.nytimes.com/2015/10/02/science/ibm-scientists-find-new-way-to-shrink-transistors.html?_r=0

Graphene
Also very high conductivities
In short term replace silicon with graphene in channel area
In long term combine graphene with other ultra-thin materials

 As of April 2013, >10 materials found; some can be
integrated with Graphene or each other
 Boron nitride (insulator) has been fabricated in
one-atom sheet as has Molybdenum Sulfide
• Molybdenum Sulfide is semiconductor, Boron Nitride is
insulator, Graphene is for interconnect
• Together one atom thick flash memory devices have been
constructed
 More recently (April 2015), three-atom thick
semiconducting films (transition metal
dichalcogenide) with wafer-scale homogeneity
have been constructed
http://thessdreview.com/daily-news/latest-buzz/flash-memory-to-be-based-on-2d-materials-a-single-atom-thick/

IBM created an array of 96
iron atoms that contain one
byte of magnetic information
in
“anti-ferromagnetic” states.
But making them is still a
major challenge………….
Source: John Markoff, New Storage Device Is
Very Small, at 12 Atoms
NY Times, Jan 13, 2012
http://www.nytimes.com/2012/01/13/science/small
er-magnetic-materials-push-boundaries-of-
nanotechnology.html

 This chip uses a million digital neurons and
256 million synapses to process information
 Potential replacement for microprocessors
 Requires completely new forms of computer
architectures and software
 For more details, see presentation on
synaptic chips:
http://www.slideshare.net/Funk98/neurosy
naptic-chips

159
Performance Improvements - IBM Cognitive Chip
From MT5009 Group Presentation, Spring 2015
http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=tVSs3tKj1tw
http://www.research.ibm.com/articles/brain-chip.shtml
http://research.ibm.com/cognitive-computing/neurosynaptic-chips.shtml#fbid=i9UhV_HagUs
http://www-03.ibm.com/press/us/en/pressrelease/44529.wss

Limitations of von-Neumann Architecture

Memristors Are the Key to AHaH Computing
Their Resistance Changes According to their History
Widely Used Ones Become Less Resistive (i.e., Learning)

http://nextbigfuture.com/2013/05/dwave-512-qubit-quantum-computer-faster.html; http://www.dwavesys.com/en/dev-tutorial-hardware.html
Quantum Computers are Also Becoming Economically Feasible:
See Session 10 on Superconductivity

Bit Energy = power consumed per clock period x number of active devices
RSFQ: rapid single flux quantum, relies on quantum effects in superconducting devices
Source: superconductivity web21, January 16, 2012. www.istec.or.jp/web21/pdf/12_Winter/E15.pdf
Improvements in Power Consumption and
Speed of Superconductors

 This is obviously a very difficult question…….
 Will all chips have 3D layers of transistors or
memory cells by 2020? How many layers of
transistors or memory cells by 2025?
 Will MRAM, PCM, ReRAM, or FeRAM replace
flash memory and which one will win?
 Will carbon nanotubes, graphene, or other ultra-
thin materials be widely used in ICs by 2025 or
2030?
 Will organic materials gain share from inorganic?
 When might Synapse chips become widespread?

 Improvements in ICs, Computers, and
Electronic Products are not over
Improvements in ICs will continue at a
rapid rate, but perhaps slower than in the
past
 New forms of Moore’s Law will Emerge
 These improvements will enable better
computers and other electronic products

 Rapid improvements in electronic products and
the Internet are not over
 Microprocessors may be slowing
 But other components and Internet are not
slowing
 Smart phone and cloud computing are future
 They will enable many new types of content and
services
• These new services will change the way work is done
• And change the definition of a business
• Big Data will become even more important

End of Moore's Law?

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