Edge Computing and the Next Generation of IoT Sensors. Alex Raimondi

Transcription

1 Edge Computing and the Next Generation of IoT Sensors Alex Raimondi

2 Who I am? Background: o Studied Electrical Engineering at ETH Zurich o Over 20 years of experience in embedded design o Co-founder of Chip-ing AG Alex Raimondi Director Embedded embedded@miromico.com Tel: It is not the answer that enlightens, but the question. - Eugene Ionesco

3 The Machine Learning Frenzy

4 Computing for the Internet of Things MEMS IMU Sense MEMS Microphone Process MCU (Cortex M) mem Transmit Short range, medium BW Low rate (periodic) data Low power Imager IOs SW update, commands 1-10 mw Long range, low BW 100 µw - 2 mw Average power needs to be in the sub-mw range Idle: Active: ~1µW ~ 50mW

5 Typical Computing Platforms Sensor Bandwidth Computational Demand Computing Platform ~ 1 bps < 1 MOPS e.g. Cortex M0 ~ 1 Kbps ~ 10 MOPS e.g. Cortex M3 ~ 100 Kbps ~ 100 MOPS e.g. Cortex M4/M4F ~ 1000 Kbps ~ 1000 MOPS???

6 Machine Learning Applications Image recognition Speech recognition Machine learning is difficult to implement: Requires large data sets Training is complex GPU s Resulting models can vary is size and complexity

7 Developing machine learning applications Building data set Training and testing model Deploying model at the edge Key technology: automatically train and deploy ML models at the edge Detecting patterns in sensor data can be used for many application scenarios Traditional hardware can already support ML with low bandwidth sensors

8 Continuous Learning [image source: branium.com]

9 Branium IoT Sensor Sensors board Accelerometer + Gyroscope Magnetometer Pressure Sensor Temperature & Humidity Proximity/Ambient Light Sensor Microphone Applications Predictive maintenance Gesture/movement detection Sound classification

10 Reducing energy consumption Long-range communication dominates energy consumption (and battery life) Sensor nodes with ML can significantly reduce the communication requirements Case 1: Data monitoring Sensors send raw data to the cloud for processing Data output: 5 KB/s Transmission: 1 pkt/min Lifetime: ~ 1 day Case 2: Pre-processing Sensors send pre-processed data to the cloud Data output: 10 B/s Transmission: 1 pkt/min Lifetime: ~ 1 month Case 3: ML at the edge Sensors send qualified data when necessary Data output: 12 B/s Transmission: 1 pkt/hour Lifetime: > 1 year

11 Miromico Ecosystem LoRa Gateways Cortex M4 based LoRa Module Capable of running Branium models Available through: Optimized for cost and power consumption Scalable production

12 Artificial Intelligence common for low bandwidth sensors Deep learning is powerful, but has greater complexity [image source: edureka.co]

13 Deep Learning in Embedded Systems? Major memory and computing challenge! [Xu et all. Nature Electronics Apr 18]

14 GAP8: Low-Power IoT Processor GAP-8 processor: 8-core PULP system Neural network based pattern matching engine Up to 8 GOPS, 300 MOPS for 1mW Commercialized by GreenWaves Technologies (French Startup) In collaboration with University of Bologna and ETH Zurich (PULP Project) GAPUINO evaluation boards available:

15 GAPuino Board: Low power QVGA camera (Himax HM01B0) HyperBus combo (DRAM/Flash 512Mbits Flash + 64Mbits DRAM) GAP8 processor with convolution hardware accelerator We are designing next generation IoT sensor nodes Near sensor analytics provided by GAP (audio and image recognition) Communication provided by our LoRa modules

16 Energy efficient hardware for next-gen IoT Sample application: CNN-based people counting We want a stand-alone camera device Example with 2.5 Ah battery: Cortex M4 Max performance: 1 frame/min 1 detection every 10 minutes Lifetime: < 2 months GAP8 Max performance: 30 frames/sec 1 detection every 10 minutes Lifetime: multiple years

17 Conclusion Machine learning is now making its way to embedded systems Vast amount of new interesting use cases Many new opportunities to bring intelligence to low power systems Miromico is investing in innovative solutions for scalable, intelligent IoT systems

18 Contact Alexander Raimondi Director Embedded Miromico AG Gallusstrasse Zurich Switzerland raimondi@miromico.ch Tel: