Eran Briman

Smart and Connected:
Can Machines Exceed Humans?
ChipEx 2016, Tel Aviv May 9, 2016
Quiz: What’s Common for:
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Connected Machines: Is it That Simple?
Multiple and constantly evolving communication standards
The IoT is composed of an almost endless list of comms standards
80
Others
70
802.15.4
60
GPS/GNSS
Cellular (Incl. M2M)
RFID (Active and Passive)
Wi-Fi
Bluetooth
Units, Billions
NFC
50
40
30
20
10
0
2014
2015
2016
2017
2018
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2019
2020
Source: ABI Research, August 2014
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But That’s Just Part of the Story
Many possible combinations of connectivity, PER DEVICE!
Google Nest Learning Thermostat
Bluetooth Low
Energy
802.15.4
(Thread)
WiFi 802.11n
Connect to
your
Smartphone
Connect to
your Home
Network
Connect
to
Internet
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Apple Watch
Bluetooth Dual Mode
Connect to
iPhone
Wi-Fi 802.11n
Speed up
data transfer
when needed
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The Ultimate Connected Device…
Wearalone Smartwatch
Other Design Considerations
Samsung Gear S2
Concurrency: Do you need to run different
protocols at the same time? Different approaches:
Your Smartphone is no longer necessary!
WiFi 802.11n
Bluetooth 4.1
1. One-time switch
For example, a device that on wake-up can be
configured to run either Zigbee or BLE
2. Time-slicing
For example, the device is continuously switching
between Zigbee and BLE, trying to avoid transmission
losses
3. Simultaneous
True multi-mode networking, requiring a multiple radios
or an SDR approach
RF integration – pros and cons
GPS
Cellular
Do you need to futureproof your design?
SW-based approach vs. lowest-cost design
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CEVA Connectivity & Communication Portfolio
CEVA provides a wide range of
connectivity platforms
LTE-MTC
LTE Cat-1, Cat-0, Cat-M
LTE, Bluetooth, Wi-Fi, 802.15.4/g
Complete solutions – HW, SW, PHY,
MAC, reference architectures
Concurrency, coexistence
Available in:
HW-based design, most powerefficient implementation
SW-based design (SDR-like),
enabling flexibility and adaptability
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How Can IoTs Get Smart(er)?
Integrate (more) sensors
Challenges:
Data
-
Always-sensing requires
extreme low power
Signal processing for
cleaning up the noise
Smart Decision
Making
Data Fusion
-
Information
Could require significant
signal processing
Memory requirements
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Artificial intelligence
algorithms
Extreme processing
requirements
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IoT Evolution: Smart Home Security
From This:
To This:
Bluetooth 4.1
Wi-Fi
802.11n
Breaking
Glass
Always-on
Face
detection
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IoT Evolution: Self Driving Cars
From This:
To This:
And This (ADAS):
8-12 Different
Image Sensors
Driver
Monitoring
Sensor
Fusion
Log/Mid
Range Radar
Ultrasound
Sensors
Car-to-Car
(V2V, V2X)
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IoT Evolution: Drones
From This (current generation):
5 cameras!!
Obstacle
avoidance
To This (next generation):
Ultrasonic
Sensors
Autonomous
Pilot
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Auto
Tracking
Depth
Mapping
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IoT Layers – Local Intelligence vs. Cloud
Local intelligence enables:
Camera/microphone/other sensors raw data does not need to be sent to
the cloud, only processed meta-data is being sent  Increased privacy
Reduced data bandwidth, transfer overhead and processing latency
to/from cloud  lower on-device power + lower data cost
Immediate and continuous availability of local processing  Quick
response for latency-critical processing, no cloud availability concerns
Efficient processing for scene analysis (sound/vision) with lower power
than GP CPU/GPU  Lower power consumption, longer battery life
Local intelligence is key for smart IoT devices !
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How Smart Can Devices Get?
1.
With 10+ sensors in an average IoT, these devices are already well-aware
2.
The Challenge: Recognizing and interpreting
Neural Networks in embedded devices
The secret ingredient: developing good training database and a lightweight neural net
OCR
General
2011
2016
Computers could not tell the difference
between a dog and a cat
Can tell the difference between
different dog breeds!
Could not run on-device (performance,
power); had to rely on cloud servers
Can run on-device by offloading the Neural Net
processing onto a dedicated vision processor
Various recognition and classification
algorithms on the device
Google Translate runs complete
Neural Net algorithm on the device
Machine Learning and Neural
Net algorithms only in the cloud
Had developed unique training database and
Neural Network that fits into embedded devices
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Back to the Quiz: What’s Common for:
Meta-data provided
with the database was
biased against gay
Training database had
flaws. Google removed the
“gorilla” tag altogether…
Very hard to meet:
“Socially Acceptable Driving”
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Driverless Cars Make Mistakes
Autonomous driving cars are the absolute challenge for robots
The scene is very difficult and fast changing, 10s of objects to analyze,
unpredictable behaviors, road conditions, weather conditions, etc’
People’s lives depend on it!
Apparently they also make mistakes:
Tesla releases an overnight over-the-air SW upgrade
Now Tesla cars drive autonomously on freeways
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Driverless Cars Make Mistakes
More autonomous cars involved in “mistakes”:
Volvo had to apologize, claiming the system wasn’t scaled…
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The Real Frontier:
Can Robots Adhere to “Socially Acceptable Rules”?
Always obey the law, must be “polite”
Driverless car would drive “like an old lady”
Cannot make eye contact, cannot see or acknowledge human gestures,
cannot notice “common knowledge hints”
Example: Merging lanes. Driverless car would never “step on the gas” to
merge in traffic
Now consider this:
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So, Can Robots Get As Smart As Humans?
1. They are always-on, always alert
No more worries about driver drowsiness
Texting while driving? Go ahead…
2. They react more quickly and more
precisely than humans
That ~1.5sec human reaction time could
be fatal in some cases
3. They can use sensors beyond human capabilities
For example, radar and ultrasonic sensors
V2V another example – reacting to things you cannot even see
BUT, with Artificial Intelligence, robots can overcome humans!!
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AlphaGo Wins Lee Sedol 4:1!!
GO (weiqi), unlike other board games, has too many possible options
for brute-force techniques
It actually requires thinking, intuition and true skills
To win, AlphaGo is using
Deep Learning techniques
Combining two separate
neural networks to narrow
down his options
AlphaGo continues learning
and improving by continuously
playing against itself
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What’s Next? Let’s Bring the Future Closer
Connect more “things”
Consider the vast choices you need to make
Enable more senses
While keeping power low for always-sensing applications
Get “things” smarter, locally
Use Machine Learning algorithms to take smart decisions
And, don’t try to reinvent the wheel!
Use systems and platforms available off-the-shelf
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CEVA’s SW Framework to Accelerate Your
Neural Network Developments
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* vs the leading GPU-based systems
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Thank You!!
Eran Briman, VP Marketing, CEVA
Email: eran.briman@ceva-dsp.com