XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes.
Angelo GarofaloGiuseppe TagliaviniFrancesco ContiLuca BeniniDavide RossiPublished in: IEEE Trans. Emerg. Top. Comput. (2021)
Keyphrases
- energy efficient
- neural network
- sensor nodes
- wireless sensor networks
- sensor networks
- energy consumption
- battery powered
- location information
- base station
- sink node
- energy efficiency
- routing protocol
- management system
- multi hop
- data aggregation
- application specific
- data transmission
- sensor data
- transmission power
- data dissemination
- multi core architecture
- data gathering
- shortest path
- routing algorithm
- cloud computing
- instruction set
- wireless communication
- real time
- network structure