In Search of the Best Machine Learning Models and Software Support for TinyML

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By Lian Jye Su | 2Q 2021 | IN-6157

 

Machine Learning Model Types for TinyML

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At the moment, the TinyML industry is still at its nascent stage. ABI Research forecasts the global shipments of TinyML devices will reach 6.3 billion by 2030, with key addressable markets including consumer devices, industrial and manufacturing, and transport & logistics, as well as smart cities applications. However, deploying Machine Learning (ML) models in TinyML application remains a very challenging task. Traditional edge AI applications are generally supported by high-performance general-purpose chipset, or by dedicated neural network accelerators. These models are process workload demanding and are not optimized for compute and power constrained terminals, such as IoT devices. In contrast, TinyML devices generally run on batteries and rely on power efficient Arm Cortex-M microcontroller (MCU) that consumes milliwatts or microwatts of power. This requires TinyML engineers to maximize the available resources in a TinyML device.

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