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.
Tinyml using neural networks for resource-constrained devices,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.