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“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibility,” a Presentation from Edge Impulse
The document discusses enabling edge machine learning (ML) for real-time applications, emphasizing improved manufacturing productivity and efficiency through advanced visual inspection systems. It highlights challenges in scaling edge ML, such as the necessity for data-centric ML approaches and optimal performance calibration on various silicon hardware. Additionally, it showcases advancements in hardware and ML algorithms that enhance object detection capabilities in constrained environments.
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“How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibility,” a Presentation from Edge Impulse
- 1. How Do WeEnable Edge ML Everywhere? Data, Reliability, and Silicon Flexibility Zach Shelby Co-founder and CEO Edge Impulse
- 2. Visual inspection systemto monitor worker safety and flag delays on the production line in real-time. Advantech increases manufacturing productivity by 15% ● A reported 15% overall increase in production line efficiency ● Faster detection of idle time raises assembly-line productivity ● Managers free up time to focus on production planning and operations 2 © 2022 Edge Impulse
- 3. State of theedge ML 2022 3 © 2022 Edge Impulse
- 4. What are thebarriers to edge ML scale? 4 © 2022 Edge Impulse
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- 6. Big data, bigmodel is a problem 6 Dataset Test Deploy Model • State of the art: Monolithic batch, from big data to big model • Developed for ML research, unsuitable for most edge ML applications © 2022 Edge Impulse
- 7. Data-Centric ML –from zero to hero 7 Test Deploy Anomaly • Auto-clustering, using feature analysis to help experts quickly label data • Auto-labeling, using pre-trained expert models to suggest labels • Active learning, using inference to drive the data collection process Model Dataset Auto-clustering Auto-labeling Active Learning © 2022 Edge Impulse
- 8. Auto-clustering in action 8 ©2022 Edge Impulse
- 9. Making ML industrialgrade
- 10. Sensor fusion forindustrial-grade ML Image Processing Sensor Audio Image ML Classifier © 2022 Edge Impulse 10
- 11. Sensor fusion forindustrial-grade ML Image Processing Sensor Audio Image ML Classifier DSP Features Post- processing ML Classifier Anomaly Detector © 2022 Edge Impulse 11
- 12. Sensor fusion forindustrial-grade ML Image Processing Sensor Audio Image ML Classifier DSP Features Multi-modal ML Classifier Anomaly Detector Post- processing Audio Features © 2022 Edge Impulse 12
- 13. • Model trainingvalidation != performance • Requires testing on the entire algorithm with real-world data for realistic performance • Understand the impact of post-processing while accounting for device constraints and latency • Choose the ideal balance between false activations and false rejections • Leverage genetic algorithms to design optimal post-processing configuration Calibrating performance at scale © 2022 Edge Impulse
- 14. ML on todayand tomorrow’s silicon
- 15. Hardware is sexyagain! 15 Sensor Audio Image Video MCU + FPU CPU GPU High-end MCU Low-end MCU RA MCU PSoC 6 © 2022 Edge Impulse
- 16. Hardware is sexyagain! 16 MCU + FPU CPU GPU High-end MCU Low-end MCU Sensor Audio Image Video Wakeup NPU NDP100 RA MCU PSoC 6 © 2022 Edge Impulse
- 17. Hardware is sexyagain! 17 MCU + FPU CPU GPU MCU + NPU Low-end MCU Sensor Audio Image Video Wakeup NPU NDP100 NDP200 Akida M55/U55 Katana RA MCU PSoC 6 © 2022 Edge Impulse xG24
- 18. Hardware is sexyagain! 18 MCU + FPU CPU + NPU NPU + GPU MCU + NPU Low-end MCU Sensor Audio Image Video TD4VA Wakeup NPU NDP200 RZ MPU NDP100 RA MCU PSoC 6 © 2022 Edge Impulse Akida M55/U55 Katana xG24
- 19. • The powerof hardware profiling • Digital twin of ML on hardware • We are combining hardware profiling with hyperparameter search – EON Tuner • Hardware-aware AutoML across data, pre- processing and ML blocks Hardware profiling & tuning 19 © 2022 Edge Impulse
- 20. FOMO: Faster objects,more objects ● Object detection on constrained compute ● 20x performance improvement ● Scales down to 100k RAM to enable MCUs ● Better at detecting smaller and more numerous objects ● Capable of segmentation and counting objects Cortex-M4 Cortex-M7 Cortex-A Nvidia FOMO 2 fps 15-30 fps 60+ fps 150+ fps SSD NA NA 3 fps 20 fps © 2022 Edge Impulse 20
- 21. Resources Increasing Manufacturing Efficiency https://casestudies.edgeimpulse.com/industrial-iot-with-advantech Constrainedobject detection: FOMO Blog docs.edgeimpulse.com/docs/tutorials/fomo-object-detection-for- constrained-devices Request a demo edgeimpulse.com/schedule-a-demo 21 © 2022 Edge Impulse
- 22. Don’t miss thesetalks! FOMO: Real-time Object Detection on Microcontrollers Jan Jongboom, Co-founder and CTO, Edge Impulse • Date: Wednesday, May 18 • Start Time: 10:50 am Deep Dive: Develop and Deploy Advanced Edge Computer Vision—Fast! Jenny Plunkett and Shawn Hymel, Snr. DevRel engineers • Date: Thursday, May 19 • Time: 9 am – 12:00 pm 22 © 2022 Edge Impulse