Efficient Spiking Neural Networks with Logarithmic Temporal Coding
This addresses efficiency for SNN applications, but it is incremental as it builds on existing temporal-coding methods.
The paper tackles the computational inefficiency of spiking neural networks (SNNs) by introducing Logarithmic Temporal Coding (LTC) and the Exponentiate-and-Fire neuron model, which reduce spike counts logarithmically and use efficient operations, achieving competitive performance at significantly lower computational cost.
A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an SNN. The conventional rate-coding method for SNNs uses the number of spikes to encode magnitude of an activation value, and may be computationally inefficient due to the large number of spikes. Temporal-coding is typically more efficient by leveraging the timing of spikes to encode information. In this paper, we present Logarithmic Temporal Coding (LTC), where the number of spikes used to encode an activation value grows logarithmically with the activation value; and the accompanying Exponentiate-and-Fire (EF) spiking neuron model, which only involves efficient bit-shift and addition operations. Moreover, we improve the training process of ANN to compensate for approximation errors due to LTC. Experimental results indicate that the resulting SNN achieves competitive performance at significantly lower computational cost than related work.