Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware.
Peter U. DiehlGuido ZarrellaAndrew S. CassidyBruno U. PedroniEmre NeftciPublished in: CoRR (2016)
Keyphrases
- low power
- recurrent neural networks
- spiking neural networks
- low cost
- feed forward
- single chip
- neural models
- vlsi architecture
- artificial neural networks
- neural network
- digital signal processing
- high speed
- low power consumption
- power consumption
- hebbian learning
- spiking neurons
- hardware and software
- biologically plausible
- biologically inspired
- back propagation
- signal processor
- real time
- gate array
- recurrent networks
- hidden layer
- image sensor
- power reduction
- neural model
- echo state networks
- learning rules
- logic circuits
- mixed signal
- visual cortex
- ultra low power
- embedded systems
- cmos technology
- motor control
- power dissipation
- training algorithm