AI applications at the very edge.

Transcription

1 AI applications at the very edge 1

2 About GreenWaves Technologies 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

3 Could that scale at IoT levels? Cloud computing + Installation costs $$$ Privacy Proprietary Information 3

4 Could that scale at IoT levels? Cloud computing Edge computing + + Installation costs $$ Bandwidth Privacy Proprietary Information 4

5 Could that scale at IoT levels? Cloud computing Edge computing + Very edge computing + Installation costs $$ Bandwidth Privacy Proprietary Information 5

6 This can scale Cloud computing Edge computing + Very edge computing + Proprietary Information Installation costs $ Operation cost $ Bandwidth Privacy 6

7 Is the HW ecosystem ready? Power supply Battery: 3.6V 3.6Ah A size battery, 2% loss per year i.e. 250uW avg for 5 years PV: 10uW per cm 2 for 50 lux indoor (ceiling) (direct sun is 100k lux) i.e. 250uW avg for 50 cm 2 12 hours a day Sensors Wireless communication Processing Proprietary Information 7

8 Sensors QVGA, 2mW VGA global shutter with logarithmic sensitivity, mW IR 80x80, 10 15mW 10Hz 2m 1mW 300uW The devil is in the detail mw image fps # frames to snap an image uw (1 image/min) QVGA ,7 VGA ,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 Proprietary Information 8

9 Wireless communication, not a simple story Proprietary Information 9

10 Processing 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 Deep sleep Data acquisition System control/light DSP DSP like Specialized computing (CNN) GAP8 (up to 11GOPS) 1uA 50uA 3 to 10 mw 20 to 80 mw + 4 to 14 mw uw (1 image/min) 10 objects recognition CNN 250 GAP8 QVGA Face Detection Viola-Jones 8 Pedestrian Detection Weak predictors objects recognition CNN 1250 VGA GAP9 10 objects recognition CNN 313 Proprietary Information 10

11 In conclusion 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) Power QVGA VGA LoRa uw 250 once a minute image sensor 7 10 objects recognition 250 Face Detection 8 Pedestrian Detection 47 image sensor objects recognition kbit/s 24 1 kbit/s 240 Proprietary Information 11

12 In real life 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

13 But are there uses for very edge computing? HD video stream Orientable Unlimited computing power QVGA to 1Mpixel images (and others) Fixed Limited computing power Proprietary Information 13

14 Applications Counting cars, bicycles Spotting stopped cars on the road side License plate reading Preventive maintenance, monitoring sound and vibrations Traffic classification Abnormal noises identification Detecting face Face recognition (few, many) Counting people in meeting room Estimating occupancy in cafeteria Hotel room entrance monitoring Retail, detecting pedestrians Proprietary Information 14

15 Applications Intrusion classification Vital signs monitoring Window shock classification Gesture recognition Key words spotting Inattention detection Proprietary 15

16 Thank you 16