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AI applications at the very edge 2018 04 26
- 2. • Founded by industry veterans in November 2014 • Based in Grenoble area, France • 16 people, going to 30 • Launched first IoT application Processor, GAP8, in February 2018 About GreenWaves Technologies
- 4. Proprietary Information 4 Could that scale at IoT levels? Cloud computing Edge computing + + • Installation costs $$ • Bandwidth • Privacy
- 5. Proprietary Information 5 Could that scale at IoT levels? Cloud computing + + Very edge computingEdge computing • Installation costs $$ • Bandwidth • Privacy
- 6. Proprietary Information 6 This can scale Cloud computing + + • Installation costs $ • Operation cost $ • Bandwidth • Privacy Very edge computingEdge computing
- 7. • Power supply • Battery: 3.6V 3.6Ah A size battery, 2% loss per year i.e. 250uW avg for 5 years • PV: 10uW per cm2 for 50 lux indoor (ceiling) (direct sun is 100k lux) i.e. 250uW avg for 50 cm2 12 hours a day • Sensors • Wireless communication • Processing Proprietary Information 7 Is the HW ecosystem ready?
- 8. Proprietary Information 8 Sensors Sensor mW image fps # frames to snap an image uW (1 image/min) QVGA 2 30 6 6,7 VGA 230 200 1 19,2 • The numbers illustrate the potential of the technology power wise • System wise, the sensors architecture and features only start to be designed for IoT use e.g. • some microphones now support wake-up-on-noise and low resolution/lower power mode • some image sensors offer multiple resolutions, but no image sensor is meant for snapping one image at the time. Their use remains a bit of DIY • Plenty of room for improvement • QVGA, 30fps @ 2mW • VGA global shutter with logarithmic sensitivity, 200 fps @ 230mW • IR 80x80, 10 fps @ 15mW • 10Hz 2m radar @ 1mW • microphone @ 300uW … The devil is in the detail
- 10. • As for sensors, raw computing power energy efficiency is not enough for IoT • IoT devices have multiple states, each of which the processor shall match to consume just the minimum necessary energy GAP8 (up to 11GOPS) • Deep sleep 1uA • Data acquisition 50uA • System control/light DSP 3 to 10 mW • DSP like 20 to 80 mW • Specialized computing (CNN) + 4 to 14 mW Proprietary Information 10 Processing uW (1 image/min) 10 objects recognition CNN 250 Face Detection Viola-Jones 8 Pedestrian Detection Weak predictors 47 10 objects recognition CNN 1250 GAP9 10 objects recognition CNN 313 QVGA VGA GAP8
- 11. Proprietary Information 11 In conclusion, a well balanced set of HW components we can support an event from once every few minutes to few times per minute, on average, for 5 years (battery) or “for ever” (PV cell) uW 250 once a minute image sensor 7 10 objects recognition 250 Face Detection 8 Pedestrian Detection 47 image sensor 19 10 objects recognition (GAP9) 313 10 kbit/s 24 1 kbit/s 240 Power QVGA LoRa VGA
- 12. • To minimize energy consumption, we also go for hierarchical device architecture, from always-on inaccurate low power sensor to higher power higher resolution sensor • e.g. • a PIR sensor detects life heat • the camera is turned on during the day (an IR camera during the night) and captures an image • the processor looks for a person in the image • a basic microphone detects a sound • the microphone array is turned on and captures an audio sequence • the processor calculates the direction of the sound and defines the window of interest • the camera is turned and captures an image • the processor looks into the window of interest for what the algorithm as been trained for Proprietary Information 12 In real life