Smart and Connected: Can Machines Exceed Humans? ChipEx 2016, Tel Aviv May 9, 2016

Transcription

1 Smart and Connected: Can Machines Exceed Humans? ChipEx 2016, Tel Aviv May 9, 2016

2 Quiz: What s Common for: 2

3 Units, Billions 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 NFC GPS/GNSS Cellular (Incl. M2M) RFID (Active and Passive) Wi-Fi Bluetooth Source: ABI Research, August

4 But That s Just Part of the Story Many possible combinations of connectivity, PER DEVICE! Google Nest Learning Thermostat Apple Watch Bluetooth Low Energy (Thread) WiFi n Bluetooth Dual Mode Wi-Fi n Connect to your Smartphone Connect to your Home Network Connect to Internet Connect to iphone Speed up data transfer when needed 4

5 The Ultimate Connected Device Wearalone Smartwatch Samsung Gear S2 Your Smartphone is no longer necessary! WiFi n Bluetooth 4.1 GPS Cellular Other Design Considerations 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 5

6 CEVA Connectivity & Communication Portfolio CEVA provides a wide range of connectivity platforms LTE, Bluetooth, Wi-Fi, /g Complete solutions HW, SW, PHY, MAC, reference architectures Concurrency, coexistence LTE-MTC LTE Cat-1, Cat-0, Cat-M Available in: HW-based design, most powerefficient implementation SW-based design (SDR-like), enabling flexibility and adaptability 6

7 Challenges: How Can IoTs Get Smart(er)? Integrate (more) sensors Data Fusion Smart Decision Making Data Information - Always-sensing requires extreme low power - Signal processing for cleaning up the noise - Could require significant signal processing - Memory requirements - Artificial intelligence algorithms - Extreme processing requirements 7

8 IoT Evolution: Smart Home Security From This: To This: Bluetooth 4.1 Wi-Fi n Always-on Breaking Glass Face detection 8

9 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) 9

10 IoT Evolution: Drones From This (current generation): To This (next generation): 5 cameras!! Obstacle avoidance Ultrasonic Sensors Autonomous Pilot Auto Tracking Depth Mapping 10

11 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! 11

12 OCR General 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 2011 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 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 12

13 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 13

14 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 14

15 Driverless Cars Make Mistakes More autonomous cars involved in mistakes : Volvo had to apologize, claiming the system wasn t scaled 15

16 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: 16

17 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!! 17

18 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 18

19 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 19

20 CEVA s SW Framework to Accelerate Your Neural Network Developments * vs the leading GPU-based systems 20

21 Thank You!! Eran Briman, VP Marketing, CEVA