Phoenix Chapter Meeting: Implementation Considerations for ML at the Edge of the Cloud


July 28, 2020



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Implementation Considerations for Machine Learning at the Edge of the Cloud

Mike STANLEY, Systems Engineer


Most machine learning classes focus on the learning algorithms, but largely ignore larger system issues. Implementation Considerations for Machine Learning at the Edge of the Cloud examines the broader set of problems that have to be dealt with before any embedded implementation can be brought to market. These range from hardware choices (MCU vs MPU, communications media, sensors, board implementation, etc), packaging issues, machine learning library choices, data collection, feature engineering, cloud interfaces, security and more. All of the above are discussed with the benefit of hindsight from an engineer tasked with putting together an end-to-end design system for embedded machine learning while working for a major semiconductor manufacturer.

Mike STANLEY, Systems Engineer


Mike Stanley spent almost four decades in the semiconductor field at Motorola, Freescale and NXP in areas ranging from circuit design to machine learning. He is author/co-author of 8 patents, numerous publications, and is a contributor to Measurement, Instrumentation and Sensors Handbook, 2nd edition. Mike was inducted into the MEMS & Sensor Industry Group Hall of Fame in 2015 and is a Senior Member of the IEEE and IEEE Standard 2700-2014 contributor. He co-authored “Sensor Analysis for the Internet of Things”, published in 2018 by Morgan & Claypool Publishers. Mike continues his association with the Sensor, Signal & Information Processing Center (SenSIP) at A.S.U. and is one of the organizers for the Phoenix Chapter of the TinyML organization.

Schedule subject to change without notice.