Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Understand

Spiking neural networks (SNNs) are biology-inspired artificial neural networks (ANNs) that comprise of spiking neurons to process asynchronous discrete signals.

• While more efficient in power consumption and inference speed on the neuromorphic hardware, SNNs are usually difficult to train directly from scratch with spikes due to the discreteness.
• As an alternative, many efforts have been devoted to converting conventional ANNs into SNNs by copying the weights from ANNs and adjusting the spiking threshold potential of neurons in SNNs.
• Researchers have designed new SNN architectures and conversion algorithms to diminish the conversion error.