Accelerating SNN Training with Stochastic Parallelizable Spiking Neurons

Understand

Spiking neural networks (SNN) are able to learn spatiotemporal features while using less energy, especially on neuromorphic hardware.

• The most widely used spiking neuron in deep learning is the Leaky Integrate and Fire (LIF) neuron.
• LIF neurons operate sequentially, however, since the computation of state at time t relies on the state at time t-1 being computed.
• This limitation is shared with Recurrent Neural Networks (RNN) and results in slow training on Graphics Processing Units (GPU).