TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers

By Pete Warden, Daniel Situnayake. 2019

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Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better (2021, methodology): “Other Tools for On-Device Inference: TF Micro [159] goes further, and consists of a slimmed down interpreter, and a smaller set of ops for inference on very low resource microcontrollers.”
The hardware lottery (2020, background): “…a “big-ger is better” race in the number of model parameters that has gripped the ﬁeld of machine learning (Amodei et al., 2018), spi-ralling energy costs (Horowitz, 2014; Strubell et al., 2019) and the dizzying requirements of deploying machine learning to edge devices (Warden & Situnayake, 2019).”
Intriguing Properties of Compression on Multilingual Models (2022, background): “, 2022) has made deployment to resource-constrained environments much more challenging (Warden and Situnayake, 2019; Samala et al., 2018; Treviso et al., 2022).”