There's a bit more description here:...

There's a bit more description here:

https://doc.brainchipinc.com/user_guide/cnn2snn.html

CNN2SNN toolkit
Overview
The Brainchip CNN2SNN toolkit provides means to convert Convolutional Neural Networks (CNN) that were trained using Deep Learning methods to a low-latency and low-power Spiking Neural Network (SNN) for use with the Akida Execution Engine. This document is a guide to that process.

The Akida Execution Engine provides Spiking Neural Networks (SNN) in which communications between neurons take the form of “spikes” or impulses that are generated when a neuron exceeds a threshold level of activation. Neurons that do not cross the threshold generate no output and contribute no further computational cost downstream. This feature is key to Akida hardware efficiency.

The Akida Execution Engine further extends this efficiency by operating with low bitwidth “synapses” or weights of connections between neurons.

Despite the apparent fundamental differences between SNNs and CNNs, the underlying mathematical operations performed by each may be rendered identical. Consequently, the trained parameters of a CNN can be converted to be Akida-compatible, given only a small number of constraints. By careful attention to specifics in the architecture and training of the CNN, an overly complex conversion step from CNN to SNN can be avoided.

The CNN2SNN toolkit comprises a set of functions designed for the popular Tensorflow Keras framework, making it easy to train a SNN-compatible network.
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