A quantized transformer and a compact Mamba model can classify indoor location with moderate accuracy within 32-64 KB model sizes, but on-device RAM usage is not measured.
A tinyml deep learning approach for indoor tracking of assets,
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Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices
A quantized transformer and a compact Mamba model can classify indoor location with moderate accuracy within 32-64 KB model sizes, but on-device RAM usage is not measured.