When the TinyML group recently convened its inaugural meeting, members had to tackle a number of fundamental questions, starting with: What is TinyML?
TinyML is a community of engineers focused on how best to implement machine learning (ML) in ultra-low power systems. The first of their monthly meetings was dedicated to defining the issue. Is machine learning achievable for low power devices such as microcontrollers? And are specialist ultra-low-power machine learning processors required?
Google Engineer Daniel Situnayake presented an overview of TensorFlow Lite, a version of Google’s TensorFlow framework designed for edge devices including microcontrollers.
Presenting the case for specialist low power application processors for ML was Martin Croome, VP Business Development, GreenWaves Technologies. Croome agreed that industry discussion of how to proceed with ultra-low-power machine learning was overdue.
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