Artificial Intelligence in Small Embedded System
•Download as PPTX, PDF•
5 likes•2,187 views
This presentation by Dov Nimratz (Solution Architect, Consultant, GlobalLogic, Lviv) and Roman Chobik (Software Engineer, Engineering Consultant, GlobalLogic, Lviv) was delivered at GlobalLogic Kharkiv Embedded Conference 2019 on July 7, 2019. During this talk, we were discussed Features of Embedded AI solutions, compared different hardware devices from Google and Intel and showed real-time Embedded AI demonstration. Conference materials: https://www.globallogic.com/ua/events/kharkiv-embedded-conference-2019/
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Artificial Intelligence in Small Embedded System
Similar to Artificial Intelligence in Small Embedded System (20)
More from GlobalLogic Ukraine
More from GlobalLogic Ukraine (20)
Recently uploaded
Recently uploaded (20)
Artificial Intelligence in Small Embedded System
- 1. 1 Embedded Artificial Intelligence Dov Nimratz & Roman Chobik Solution Architect June 2019
- 3. 3 Artificial Intelligence Revenue by Region Three major markets - Asia Pacific, North America, Western Europe. Artificial Intelligence is a catalyst to enter various customer projects. Technical capability of embedded AI has appeared at the beginning of 2019 and now it is a technology edge.
- 4. 4 Embedded AI Market Artificial Intelligence SoftBank/Aldebaran-Pepper Artificial Intelligence Analytics US$70bn market by 2020 Agriculture Amazon-Bosch-BoniRob Agricultural Robot $16.3bn market by 2020 Autos Google - Self driving car Autonomous Car $87bn market by 2020 Financial Robo-advisor $225bn-2.2tn AuMby 2020E Medical da Vinci Surgical System Surgical US $18bn market by 2022 Bionics Indego-exoskeleton Exoskeletons $2.1bn market by 2021E Inference at the edge. Less computation in the Cloud, less data transfer, real-time reaction. Offline Inference. Frequent internet connection does not exist or it can not connect over video content. US $67bn robot market by 2025
- 5. 5 Industrial Yaskawa Motoman Industrial robots US$24bn market by 2020E Logistics, packaging, materials S$31bn market by 2020E` Home Robot-gutter cleaner $12.2bn market by 2018 Entertainment Lego-Mindstorm Entertainment & Leisure US$7.6bn market by 2018 Military QinetiQ-MAARS Exoskeletons $7.5bn market by 2018E UAV/UGV/UUV (Drones) Northrop-Global Hawk Drones US$14bn market by 2025 Healthcare Fraunhofer-Care-O-bot-4 Rehabilitation $1.1bn market by 2021E Personal & Care-bots US$17.4bn market by 2020E` Embedded AI Market Real-time. Immediate reaction for situation is very common. Cloud computation can not guarantee it. Privacy. Some information is forbidden to send from the edge device before the critical issue happened.
- 6. 6 Embedded Artificial Intelligence use cases ● Secure access control ● Actuators driving for different animal types ● Counting animals Security camera in yard ● Intrusion detection ● Barrier integrity control ● Early warning alarm Restricted area secure ● Sorting garbage or waste ● Integrity control ● Completeness check Industry or retail sorting ● Secure for employees ● Much chipper ● Detect and measure better than human Construction inspection
- 7. 7 Embedded AI Technologies Matrix Hardware General Purpose AI Accelerator AI Hardware Raspberry PI Intel UP Google Coral AI Accelerator Intel Neural Network Stick 2 Google Coral Dev Board NVIDIA Jetson Nano
- 8. 8 Embedded AI Platforms Research • NVIDIA Jetson Nano • Intel Neural Network Stick 2 • Google USB TPU Accelerator • Google Coral Dev Board
- 9. 9 Comparison Summary Google Coral TPU accelerator NVIDIA Jetson Nano Intel Up Google Coral Dev Board Intel Neural Network Stick 2 Tested two AI models: • MobileNet SSD v1 • MobileNet SSD v2 Reference hardware: • MacBook pro • Intel Up/Raspberry PI Testing conditions: • Image size 300x300 • Average inferencing time taken
- 10. 10 Benchmark Comparison Resume: • Absolute winner is Coral Dev Board. • Intel NN Stick is effective at face recognition related projects. • Coral USB Accelerator is the cheapest solution with a good performance. • NVIDIA Jetson Nano is not recommended for use. Coral dev board Coral USB accelerator Intel NN Stick 2 NVIDIA Jetson Nano TF-TRT NVIDIA Jetson Nano TF MacBook Pro Raspberry PI
- 11. 11 Embedded AI Hardware Intel Neural Network Stick • Price $88 • Size 75x27x14 mm • Weight 18 gram • Based on Myriad X VPU • 16 video CPU • MobileNet v2 20 fps • TensorFlow, Cafee • Current up to 14 mA NVIDIA Jetson Nano • Price $100 • Size 95x76x30mm • Weight 136 gram • 64bit quad-core A57 • 128-core Nvidia GPU • MobileNet v2 12 fps • Current up to 1220 mA Google TPU Accelerator • Price $75 • Size 65x30x8 mm • Weight 12 gram • Google Edge TPU • MobileNet v2 40+ fps • TensorFlow Lite • Current up to 9 mA Google Coral Dev Board • Price $150 • Size 85x56x20mm • Weight 78 gram • NXP i.MX 8M SOC • Google Edge TPU • MobileNet v2 100+ fps • TensorFlow Lite • Current up to 960 mA
- 12. 12 AI Models and Tools
- 13. 13 Confidential Software Frameworks: ● TensorFlow ● TensorFlow Lite ● Cafee Optimizations: ● Quantization ● Tensor Fusion MobileNet SSD SqueezeNet Inception Models: ● MobileNet SSD v1 & v2 ● SqueezeNet 1.0, SqueezeNet 1.1 ● Inception v1 & v2 & v3 3 & v4 Libraries: ● OpenCV ● OpenVINO QuantizationTensorRT Optimisation
- 14. 15 Confidential • Offline Inference • Low Bandwidth -> Low Power -> Long battery life • Minimal latency - Real Real-Time • Privacy and security custom data • Less Cloud size -> Less expenses • Edge to Edge communication AIoT - from Cloud to Fog computing