Bridging the Gap Between Spiking Neural Networks & LSTMs for Latency & Energy Efficiency.
Gourav DattaHaoqin DengRobert AvilesZeyu LiuPeter A. BeerelPublished in: ISLPED (2023)
Keyphrases
- energy efficiency
- spiking neural networks
- response time
- biologically inspired
- power consumption
- wireless sensor networks
- energy consumption
- data center
- sensor networks
- energy efficient
- spiking neurons
- learning rules
- feed forward
- routing protocol
- biologically plausible
- artificial neural networks
- smart home
- neural network
- power management
- motor control
- sensor nodes
- training algorithm
- computer vision
- radial basis function
- cost effective
- routing algorithm
- sensor data
- back propagation
- training set
- data streams
- support vector
- machine learning
- data sets