Smart and Connected: Can Machines Exceed Humans?

Smart and Connected:

Can Machines Exceed Humans?

ChipEx 2016, Tel Aviv May 9, 2016

http://www.ceva-dsp.com/














2 CEVA Proprietary Information

Quiz: What’s Common for:


Connected Machines: Is it That Simple?

Multiple and constantly evolving communication standards

The IoT is composed of an almost endless list of comms standards

0

10

20

30

40

50

60

70

80

2014 2015 2016 2017 2018 2019 2020

Un

its,

Bill

ion

s

Others

802.15.4

NFC

GPS/GNSS

Cellular (Incl. M2M)

RFID (Active and Passive)

Wi-Fi

Bluetooth

Source: ABI Research, August 2014


But That’s Just Part of the Story

Google Nest Learning Thermostat Apple Watch

Connect to

iPhone

Speed up

data transfer

when needed

Many possible combinations of connectivity, PER DEVICE!

Bluetooth Dual Mode Wi-Fi 802.11n Bluetooth Low

Energy WiFi 802.11n

802.15.4 (Thread)

Connect to

your

Smartphone

Connect

to

Internet

Connect to

your Home

Network


The Ultimate Connected Device…

Wearalone Smartwatch

Concurrency: Do you need to run different

protocols at the same time? Different approaches:

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

Do you need to futureproof your design?

SW-based approach vs. lowest-cost design

Samsung Gear S2

Your Smartphone is no longer necessary!

Other Design Considerations

Bluetooth 4.1 WiFi 802.11n

Cellular GPS


CEVA Connectivity & Communication Portfolio

CEVA provides a wide range of

connectivity platforms

LTE, Bluetooth, Wi-Fi, 802.15.4/g

Complete solutions – HW, SW, PHY,

MAC, reference architectures

Concurrency, coexistence

Available in:

HW-based design, most power-

efficient implementation

SW-based design (SDR-like),

enabling flexibility and adaptability

LTE-MTC

LTE Cat-1, Cat-0, Cat-M


How Can IoTs Get Smart(er)?

Integrate (more) sensors

Challe

nges:

- Always-sensing requires

extreme low power

- Signal processing for

cleaning up the noise

Data

Data Fusion

- Could require significant

signal processing

- Memory requirements

Information

Smart Decision

Making

- Artificial intelligence

algorithms

- Extreme processing

requirements


IoT Evolution: Smart Home Security

From This: To This:

Face

detection

Always-on Wi-Fi

802.11n

Breaking

Glass Bluetooth 4.1


IoT Evolution: Self Driving Cars

From This: To This:

Ultrasound Sensors

Sensor Fusion

Driver Monitoring

Log/Mid Range Radar

8-12 Different

Image Sensors

Car-to-Car (V2V, V2X)

And This (ADAS):


IoT Evolution: Drones

From This (current generation): To This (next generation):

Auto Tracking

Ultrasonic Sensors

Obstacle avoidance

Autonomous Pilot 5 cameras!!

Depth Mapping


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 !


How Smart Can Devices Get?

2011

Can tell the difference between

different dog breeds!

Can run on-device by offloading the Neural Net

processing onto a dedicated vision processor

Google Translate runs complete

Neural Net algorithm on the device

Had developed unique training database and

Neural Network that fits into embedded devices

Computers could not tell the difference

between a dog and a cat

Could not run on-device (performance,

power); had to rely on cloud servers

Various recognition and classification

algorithms on the device

Machine Learning and Neural

Net algorithms only in the cloud

2016

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

OC

R

Genera

l


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”


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


Driverless Cars Make Mistakes

More autonomous cars involved in “mistakes”:

Volvo had to apologize, claiming the system wasn’t scaled…


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:


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!!


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


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


CEVA’s SW Framework to Accelerate Your Neural Network Developments

* vs the leading GPU-based systems

Eran Briman, VP Marketing, CEVA

Email: [email protected]

Thank You!!





























mailto:[email protected]



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Smart and Connected: Can Machines Exceed Humans?