We held our next tinyML Talks webcast. Mahesh Chowdhary fromST Microelectronics presented Smart motion sensors offer a world of always-on possibilities: TinyML use cases and applications on December 13, 2022.
Smart motion sensors are enabling distributed machine learning to significantly reduce bandwidth for more responsive, energy-conscious edge computing solutions. ST’s latest ultra-low-power 6-axis inertial sensor (LSM6DSV16X) comes with a Machine Learning Core (MLC) and Finite State Machine (FSM) to enable motion pattern recognition or vibration detection. These smart inertial sensors have the capability to execute decision trees in the built-in MLC of the sensor which is ideal for always-on applications for wearables or wireless sensor nodes with a current consumption of only a few microamps.
To help companies stay ahead of the design curve and get their products to market quicker, powerful neural network conversion tools like our STM32Cube.AI make it easy to import and convert pre-trained Machine Learning or Neural Network models into optimized C code for STM32. In addition, middleware is available for easy integration with popular mobile platforms, such as Android, which are commonly used to build smart devices for consumer, industrial, and automotive applications.
Mahesh Chowdhary, Ph.D. is a Fellow and Senior Director of MEMS Software Solutions at STMicroelectronics based in Santa Clara CA. He leads effort on development of solutions and reference designs for mobile phones, consumer electronic devices, automotive and industrial applications that utilize MEMS products, computing and connectivity products. His area of expertise includes AI/ML, MEMS sensors, IoT, digital transformation, and location technologies. He has been awarded 34 patents. Mahesh received PhD in Applied Science (Particle Accelerators) from the College of William & Mary in Virginia.
Watch on YouTube:
Download presentation slides:
Feel free to ask your questions on this thread and keep the conversation going!