Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:technology
|
| gptkbp:appliesTo |
gptkb:consumer_electronics
gptkb:home_automation industrial automation logistics robotics asset tracking fitness trackers remote monitoring environmental monitoring energy management security systems image classification environmental sensing gesture recognition hearing aids anomaly detection predictive maintenance smart cities voice assistants healthcare devices automotive sensors audio recognition agriculture sensors |
| gptkbp:book |
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
|
| gptkbp:conference |
tinyML Summit
|
| gptkbp:containsCommunity |
tinyML Foundation
|
| gptkbp:enables |
edge computing
real-time data processing on-device inference privacy-preserving AI low-power AI offline AI applications |
| gptkbp:field |
gptkb:machine_learning
|
| gptkbp:focusesOn |
deploying machine learning models on microcontrollers
|
| https://www.w3.org/2000/01/rdf-schema#label |
TinyML
|
| gptkbp:relatedTo |
gptkb:Edge_AI
Embedded AI |
| gptkbp:requires |
small memory footprint
ultra-low power consumption |
| gptkbp:supportsLanguage |
gptkb:TensorFlow_Lite_Micro
gptkb:CMSIS-NN Arduino ML libraries MicroTVM uTensor |
| gptkbp:usedIn |
IoT devices
embedded systems wearables smart sensors |
| gptkbp:bfsParent |
gptkb:Thumb-2_(subset)
|
| gptkbp:bfsLayer |
6
|