Overview. Background. Intelligence at the Edge. Learning at the Edge: Challenges and Brainstorming. Amazon Alexa Smart Home!

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2 Overview Background Intelligence at the Edge Samsung Research Learning at the Edge: Challenges and Brainstorming Amazon Alexa Smart Home!

3 Background Ph.D. at UW CSE RFID, Mobile, Sensors, Data Nokia Research Samsung Research Silicon Valley Context Framework On-Device Analytics AlgoSnap Machine Learning at Amazon Alexa Smart Home

4 Intelligence at the Edge Edge End-points that generate data Social, Financial, Physical Sensors, Environmental Sensors, Health Sensors, Car, Home, Television, Media Consumption, Searches, Location, Calendars, Purchases

5 All Data All Intelligence Shared back to devices All Data...

6 All Data All Intelligence Shared back to devices All Data Why Not? Energy, latency, privacy...

7 IoT Increased Volume, Variety, Velocity! Radio Energy Smartphone Data Type Sensor Types Estimated Avg Location GPS, WiFi, Cell Towers, Bluetooth 40K / min Device Motion Accelerometer, Gyroscope, Compass 160K / min Network Latency Environmental Device Interaction Camera, microphone, Light, proximity, temperature, pressure, magnetic field keys pressed, touch screen, App usage, media usage, screen on/off, etc 37MB / min no video: (960K / min) 20K / min Privacy Social Calls, SMS, s, Facebook, Twitter, Calendar, contacts 1K / min Interest/content Browser, search, purchases, bookmarks 20K / min Wearables Sensor + interaction data from wearables 160K-180K / min Purchases Web transactions, NFC transactions 1K / min 1.5 to 40 MB/min per user ~1 Exabyte/day at Facebook Scale

8 Intelligence at the Edge Save Energy: Push computation to the data Reduce Latency: Run models on the user s device Enhance Privacy: Don t upload data

9 Example: Centaurus - Edge Framework N N+1 N+2... Concept: shift data and processing to the device-side

10 Example: Centaurus - Edge Framework Only High-Level Context is Sent to the Cloud, with Consent Concept: shift data and processing to the device-side

11 Example: Centaurus - Edge Framework Expressive scripts specified as dataflows Operators transform raw data Models trained in cloud with big data set On Device: Intelligence Script Engine Context Scripts configure Operators Examples: Avg Band-Pass Naïve Bayes Segment Duration Entropy Min N-gram Std Dev Max Count Median Pattern Magnitude Match Tokenize Energy Difference Tokenize Decision FFT Tree Correlation Similarity Sum Euclidean Dist. Script for Watching a Movie Script for Walking Library of data Processors Example: Gyro Data FFT FFT of Gyro Modules to connect to data sources (sensors, logs, social networks)

12 Example: Centaurus - Edge Framework Quantifying savings: Walking Detection on smartphone 20 hours 10Hz Implement with Centaurus - uploads only classification Implement in Cloud uploads all data Centaurus uploads only 0.14% of the data Centaurus time-to-classification is slightly faster Power consumption (network is biggest power hog): WLAN Upload Data Size (Kb) Power (mj) Time (ms) mj/kb Centaurus Cloud-Only 55,487 66,827 84, HSPA Upload Data Size (Kb) Power (mj) Time (ms) mj/kb Centaurus , Cloud-Only 55,487 1,048, ,826 15

13 The Next Step: Learning at the Edge Push training to the Edge, not just models Raw data never leaves the Edge! Challenges and brainstorming: 1) Decentralized learning: general + personal models Federated Learning Google Research Blog April 6,

14 The Next Step: Learning at the Edge Push training to the Edge, not just models Raw data never leaves the Edge! Challenges and brainstorming: 1) Decentralized learning: general + personal models 2) Adapting to varying device resources 3) Security and privacy between device and cloud 4) Supervised learning: soliciting user labels

15 The Next Step: Learning at the Edge Push training to the Edge, not just models Raw data never leaves the Edge! Challenges and brainstorming: 1) Decentralized learning: general + personal models 2) Adapting to varying device resources 3) Security and privacy between device and cloud 4) Supervised learning: soliciting user labels 5) Peer-to-Peer coordination at the Edge

16 Amazon Alexa Smart Home 200 Engineers and Scientists and growing fast Petabyte-scale data: millions of customers & devices Hiring Engineers and Scientists at all levels! Contact: