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Inference at the Edge: A Case Study at the Amazon Spheres (ARC318) - AWS re:Invent 2018

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In this talk, we consider the unique challenges of the biosphere that recently opened in downtown Seattle. We address two of those challenges using modern deep learning techniques: computer-vision-based plant health monitoring, and microclimate anomaly detection using autoencoders on time-series data extracted from multiple sensors. Our focus is on architecting the inference pipelines for solving these problems at scale. Specifically, we highlight the inference steps and TensorRT optimizations for AWS Greengrass ML inference.

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Inference at the Edge: A Case Study at the Amazon Spheres (ARC318) - AWS re:Invent 2018

  1. 1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference at the Edge: A Case Study at the Amazon Spheres WenMing Ye Specialist Solution Architect Amazon A R C 3 1 8 Miro Enev, PhD Sr. Solution Architect NVIDIA
  2. 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Introduction: AI @ Amazon Spheres Video: Welcome to the Amazon Spheres [ living wall video ] Approach: Anomaly detection using DL on time-series sensor streams Architecture: Training (SageMaker) Inference (NVIDIA Jetson TX2) Results: Improved alerting Future Work: Computer vision based plant stress
  3. 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  4. 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  5. 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Our Goal = Help the Caretakers “We take care of 40,000 plants from over 700 species!” Claire Ben
  6. 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Sensor Types
  7. 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Temperature
  8. 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Co2
  9. 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. InstLightLevels
  10. 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Challenge 1: Lots of Systems to Manage
  11. 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Challenge 2: Too Many Suspicious Values
  12. 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. When Issues Occur…They Go Unnoticed Example 1: During a product launch (Alexa microwave integration), event organizers requested that the temperature be lowered for media and the air velocity reduced for better acoustics Problem: Incorrect temp. and air velocity for fourth floor plants for a week Example 2: Building automation staff suspended the irrigation for the living wall to update/repairs several sensors Problem: 24 hours without water for living wall [ low irrigation pressure warning was ignored ]
  13. 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  14. 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AI to Assist the Caretakers • Accurate Alerts [ low false alarm rate ] • Real-time & Low Cost • Enable Current/Future Science • Scalability & Availability of Technology
  15. 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AI ML DL AI
  16. 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. ML TRIBES
  17. 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Why DL
  18. 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deep Learning @ Spheres [ AutoEncoder Network ]
  19. 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Preparing Data for Model Training Split into sliding windows [ heavily overlapped ] Z Normalization
  20. 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Detecting Anomalies Reconstruction error (RE) as a proxy to outliers Whenever RE is high, get an alert
  21. 21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Multi-Sensor Models [ AutoEncoder Network ]
  22. 22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Living Wall Inside the Spheres [ First floor ] Cafe North Conservatory
  23. 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Aircuity Sensors AC-46-1-2 AC-46-1-1 AC-46-1-4 AC-46-1-3 AC-46-1-5
  24. 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Sensor Zones Living Wall [ 4 floors ] t,rh,d,co2 [ X, AC-46-2-2, AC-46-3-2, AC-46-4-3 ] light level [ DLI-46-1-DG1, DLI-46-2-DG2, DLI-46-3-DG3, DLI-46- 4-DG5 ] North Conservatory [ 1st floor ] t,rh,d,co2 [ AC-46-1-1, AC-46-1-2, AC-46-1-3, AC-46-1-4 ] light level [ DLI-46-1-DS1, DLI-46-1-DM2, DLI-46-1-DM3 ] South Conservatory [ 2nd floor ] t,rh,d,co2 [ AC-46-2-3, AC-46-2-4, AC-46-2-5, AC-46-2-6 ] light level [ DLI-46-2-DM1, DLI-46-2-DM2, DLI-46-2-DM3 ] Canopy [ 3 floors above N. Conservatory ] t,rh,d,co2 [ AC-46-2-1, AC-46-3-1, AC-46-4-2 ] light level [ DLI-46-4-DL1, DLI-46-4-DL2, DLI-46-4-DL3, DLI-46-4-DL4, DLI-46-4-DL5, DLI-46-4-DL6, DLI-46-4-DL7, DLI-46-4-DL8, DLI-46-4-DL13, DLI-46-4-DL14 ]
  25. 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  26. 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Sensors SpheresAIArchitecture v01 Camera CO2 Light Level Thermostat AWS Cloud Model_1 Model_2 Model_N
  27. 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Amazon SageMaker
  28. 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Cloud AWS Cloud SpheresAIArchitecture v01 On-Premise Email alert Sensors Camera CO2 Light Level Thermostat
  29. 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference @ Edge NVIDIA Jetson TX2 Scaling Inference [ edge processing ]
  30. 30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  31. 31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Jupyter Notebook
  32. 32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  33. 33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  34. 34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  35. 35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  36. 36. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. WenMing Ye - wye@amazon.com Miro Enev - menev@nvidia.com
  37. 37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  38. 38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Types of ML/DL Used
  39. 39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Model Training
  40. 40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Anomaly Detection
  41. 41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Anomaly Detection
  42. 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. DL Anomaly Detection [ Autoencoder Network ]
  43. 43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Spectral Matching

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