Further down the page: Direct quantization of a standard Keras...

Further down the page:

Direct quantization of a standard Keras model (also called post-training quantization) generally introduces a drop in performance. This drop is usually small for 8-bit or even 4-bit quantization of simple models, but it can be very significant for low quantization bitwidth and complex models.

If the quantized model offers acceptable performance, it can be directly converted into an Akida model, ready to be loaded on the Akida NSoC (see the convert function).

However, if the performance drop is too high, a quantization-aware training is required to recover the performance prior to quantization. Since the quantized model is a Keras model, it can then be trained using the standard Keras API.Note that quantizing directly to the target bitwidth is not mandatory: it is possible to proceed with quantization in a serie of smaller steps. For example, it may be beneficial to keep float weights and only quantize activations, retrain, and then, quantize weights.