tinyML Talks webcast: 1) Enabling Neural network at the low power edge 2)Amber: A Complete, ML-Based, Anomaly Detection Pipeline forMicrocontrollers
" Enabling Neural network at the low power edge: A neural network compiler for hardware constrained embedded system"
Vice President of Technology
Neural Networks continue to gain interests for deployment in IoT and other mobile and edge devices. Yet enabling a NN in a hardware constrained embedded system such as low power edge devices presents many challenges.
In this presentation we will show how Eta Compute took an integrated approach to minimize the barrier to design neural network for ultra-low power operation, with an example for embedded vision application:
* Neural network design and optimization for the embedded world: memory, compute power and accuracy
* Hardware and software co-optimization to improve the energy efficiency
* Automatic inference code generation based on the model graph by a proprietary hardware-aware compiler tool
The audience will gain an understanding of the integrated approach.
"Amber: A Complete, ML-Based, Anomaly Detection Pipeline for Microcontrollers"
Brian Turnquist - CTO
Rodney Dockter - Director of Computer Vision
Sensor anomaly detection pipelines deployable on microcontrollers typically begin with data collection which is followed by off-line training and model-building on multi-core, high performance compute resources. The resulting model is static and may require additional pruning prior to deployment. Furthermore, the model may not translate to other sensors, even identical sensors monitoring identical assets running the same motion profiles. This talk will demonstrate a complete, unsupervised machine learning-based, anomaly detection pipeline that is deployable on low-power microcontrollers such as the ARM Cortex M7. Using live sensor values in real-time, the Amber algorithm seamlessly tunes its hyperparameters, then trains its ML model, and finally transitions to anomaly detection mode where it can generate thousands of inferences per second with extremely high accuracy.