Schedule
Timezone: PST
Enabling Neural network at the low power edge: A neural network compiler for hardware constrained embedded system
Chao XU, VP of Technology
Eta Compute
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 (hardware/ software considerations) and an understanding of what is possible in terms of efficiency on modern sensor node processors for vision.
Chao XU, VP of Technology
Eta Compute
Chao Xu brings more than 20 years of experience in the advanced signal processing and machine learning, networking semiconductor, and silicon photonics. Prior to Eta Compute, Dr. Xu served as senior director of communication systems and computing and storage platform at Inphi Corporation. Prior to Inphi, he held senior R&D positions at Integrated Device Technology and PMC-Sierra. Dr. Xu has over 30 pending and awarded patents. He received his Ph.D. from the University of Pennsylvania, a master’s of science degree and a bachelor of engineering degree in electrical engineering from the University of Science and Technology of China. His research area includes speech recognition, noise robustness, feature extraction, and other general machine learning methods.
Timezone: PST
Amber: A Complete, ML-Based, Anomaly Detection Pipeline for Microcontrollers
Rodney DOCKTOR, Director of Vision and Robotics
BoonLogic
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. Since each microcontroller autonomously customizes its ML model to its associated sensor, this approach is suitable for deployments to billions of IoT sensors.
Brian TURNQUIST, CTO
BoonLogic
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. Since each microcontroller autonomously customizes its ML model to its associated sensor, this approach is suitable for deployments to billions of IoT sensors.
Rodney DOCKTOR, Director of Vision and Robotics
BoonLogic
Rodney Dockter is an engineer with a broad background in robotics and machine learning. Application areas include industrial automation, autonomous off-highway vehicles, mobile robotics, and surgical robotics. Rodney is the Director of Computer Vision at Minneapolis-based Boon Logic, and a lecturer at the University of Minnesota. He holds a Ph.D. in Mechanical Engineering from the University of Minnesota.
Brian TURNQUIST, CTO
BoonLogic
Brian Turnquist has worked in machine learning for the past twenty years developing numerous novel algorithms for automatically clustering biological signals in real-time. Turnquist is CTO of Minneapolis tech start-up, Boon Logic, and a previous visiting researcher at the Universities of Nürnberg and Heidelberg, and tenured professor at Bethel University. His Ph.D. is in Mathematics from the University of Maryland with fourteen refereed publications in neuroscience and mathematics.
Schedule subject to change without notice.