# Overview of the optimalisation techniques
During this project two specific optimalisation techniques were investigated. On the one hand quantisation, on the other hand pruning. Next to these two optimalisation techniques there are other ways to improve and accelerate your Deep Learning model when running it on an edge device. These methods are explained further.
# Quantisation
Quantisation is an optimalisation step where the weights and bias values of the neural network are converted to a more memory space efficient format. Mostly this conversion is between 32-bit floating point values to 8-bit integer values. This step can improve the memory usage by a factor of four at the most, because it uses four times less bits. It should also improve inference time because the operations used by the microcontroller are 8-bit based instead of 32-bit based. Because of this conversion, the model can perform worse after quantisation.
# Pruning
# Other optimalisation techniques
Next to quantisation and pruning there are othwer ways to optimise the running speed of the model. One could be by optimising the compiler settings, use a co-processor if the target has one, or go to another type of device architecture.
# Separable convolution
Disadvantage: Because the separable convolution reduces the number of parameters for a convolution operation, if your network is already small, you might end up with too few parameters and your network might fail to properly learn during training. If used properly, however, it manages to enhance efficiency without significantly reducing effectiveness.
A good example with separable convolutions can be found here.