On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers

On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is a resource-intensive task, making the implementation and execution of DNN training algorithms on MCUs challenging due t...

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Veröffentlicht in:	IEEE transactions on computer-aided design of integrated circuits and systems 2024-10, p.1-1
Hauptverfasser:	Deutel, Mark, Hannig, Frank, Mutschler, Christopher, Teich, Jurgen
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Design automation Efficient AI Embedded Machine Learning Inference algorithms Memory management Microcontrollers On-Device Training Quantization (signal) Random access memory Servers Tensors TinyML Training
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