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April 28, 2022

MSIG Sensor Integration Master Class

Earn CEUs and IEEE PDHs from this hands-on SEMI MSIG Master Class & Lab, where instructors will explain and demonstrate how AI/ML logic can be implemented on edge devices such as smart sensors. Attendees will build and operate their own edge device with AlgoBuilder tools in 2 lab sections of the course.

This course is designed for applications engineers wanting to learn how to add sensors to an existing or new product. Instructors are experienced STMicroelectronics engineers with many sensor design and implementations.

The course covers many topics including the importance of power efficiency, latency, and bandwidth considerations for AI/ML implementation on edge devices. Learn how computing can be distributed between the edge devices and the cloud. The latest trends and applications of smart sensors in consumer electronics, automotive, and industrial use cases will also be discussed.

Join us in person at SEMI HQ, for this hands-on learning experience. 

This course is underwritten by STMicroelectronics.

ST Logo

Time

8:30 am - 5:30 pm

Location

SEMI
673 South Milpitas Avenue
Milpitas, CA 95035
United States

MSIG Sensor Integration Master Class

Course Abstract:

This class will explain and demonstrate how AI/ML logic can be implemented on Edge devices such as Smart sensors. Power efficiency, latency, and bandwidth considerations are important for AI/ML implementation on Edge devices. Computing can be distributed between Edge devices and Cloud. The latest trends and applications of smart sensors in consumer electronics, automotive, and industrial use cases will be discussed.

Course Outline:

  1. AI / ML on Edge devices
    1. Why AI / ML on Edge devices?
      1. Power efficiency, latency and bandwidth considerations when executing AI / ML logic on Edge devices.
    2. Computing distribution between Edge device, gateway and Cloud.
    3. Assignment: Finite State Machine and Decision Tree applications
  2. Introduction to Inertial Sensors with AI / ML capabilities
    1. Background on inertial sensors including applications
    2. Typical performance characteristics of inertial sensors
    3. Lab: SensorTile.Box and use of custom sensors to change sensor sampling rate, filters, and other configuration. 
  3. Machine Learning Core (MLC) in Smart Sensor
    1. An introduction ML at Edge of the Edge, Smart Sensors: Latest trends Applications of Smart sensors applications in consume electronics, automotive, industrial use cases. Next generation of smart sensors.
    2. AI on the Edge and requirements of distributed intelligence system.
    3. Introduction to MLC framework
      1. Input data
      2. Filters and Feature selection
      3. Optimization
      4. Tools
    4. Rapid Prototyping with MLC: current consumption under 10 uA
    5. Lab: Motion Intensity detection using MLC. Lab conducted using AlgoBuilder tool.  
  4. Finite State Machines (FSM) in Smart Sensor
    1. Introduction to FSM
      1. Input data
      2. FSM definition and structure
      3. Conditions list
      4. Tools
    2. Rapid Prototyping using FSM:
    3. Lab: Gesture recognition using FSM. Lab conducted using AlgoBuilder Tool.

Featured Speakers

Mahesh Chowdhary
Mahesh Chowdhary, Ph.D.
Fellow & Director of Strategic Platforms & IoT Excellence Center
STMicroelectronics
Mahaveer Jain
Mahaveer Jain
Applications Principal Engineer
STMicroelectronics
Dennis Cioccca
Denis Ciocca
Staff Applications Engineer
STMicroelectronics

Registration Details

During Registration, you will have the option to also register for MEMS & Sensors Technical Congress (April 26-27) and the Positing, Navigation & Timing Gap Analysis Workshop (April 25).  3 Great Opportunities to Network, Learn, Share and Connect in 1 week.

CANCELLATION POLICY:

  • Substitution available anytime with written note from original registrant.
  • 75% Refund is cancelled before April 15, 2022. 
  • 50% Refund if cancelled between April 16 and date of workshop.
  • No refunds after April 28.

Speaker Bios

Mahesh Chowdhary, Ph.D. is a Fellow and Director of Strategic Platforms & IoT Excellence Center 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 sensors, computing and connectivity products. His area of expertise includes AI/ML, MEMS sensors, IoT, digital transformation, and location technologies. He has been awarded 30 patents. He has spoken extensively internationally about Machine Learning, Smart Sensors, and IoT. Mahesh received PhD in Applied Science (Particle Accelerators) from the College of William & Mary in Virginia. He is also an Adjunct Professor at IIT, Delhi.

Mahaveer Jain - Mahaveer Jain is Application Principal Engineer at STMicroelectronics(Santa Clara, CA) and specializing in MEMS sensors, Algorithm, DSP, and Machine Learning . Over the course of his career, Mahaveer worked on indoor navigation, hybrid positioning , sensor calibration, and sensor fusion. His most recent work has been developing extremely low power machine learning models to run on sensors. Mahaveer received a Bachelor of Technology in Physics from IIT Delhi.

Denis Ciocca - Denis is Staff Applications Engineer at STMicroelectronics specializing in Linux OS, Linux device drivers, Android OS, and Smart sensors. He has developed a variety of solutions with MEMS sensors, a computational platform of STM32 microcontrollers and wireless connectivity solutions. Denis has received his Master’s degree in Computer Science and Engineering from the University of Pavia, Italy.