Energy-efficient FPGA Spiking Neural Accelerators with Supervised and Unsupervised Spike-timing-dependent-Plasticity.
Yu LiuSai Sourabh YenamachintalaPeng LiPublished in: ACM J. Emerg. Technol. Comput. Syst. (2019)
Keyphrases
- energy efficient
- spike trains
- spiking neural networks
- field programmable gate array
- biologically plausible
- semi supervised
- supervised learning
- unsupervised methods
- unsupervised learning
- wireless sensor networks
- biologically inspired
- single chip
- learning rules
- energy consumption
- sensor networks
- feed forward
- spiking neurons
- neuron model
- bio inspired
- energy efficiency
- base station
- data dissemination
- routing protocol
- computing systems
- hardware implementation
- high speed
- hardware architecture
- hebbian learning
- sensor nodes
- visual cortex
- artificial neural networks
- image processing algorithms
- network architecture
- low cost
- data transmission
- embedded systems
- multi core architecture
- parallel computing
- data sets
- associative memory
- neural network model
- power consumption
- sensor data
- mobile phone