  Light binary convolutional neural networks (LB-CNN) are particularly useful
when implemented in low-energy computing platforms as required in many
industrial applications. Herein, a framework for optimizing compact LB-CNN is
introduced and its effectiveness is evaluated. The framework is freely
available and may run on free-access cloud platforms, thus requiring no major
investments. The optimized model is saved in the standardized .h5 format and
can be used as input to specialized tools for further deployment into specific
technologies, thus enabling the rapid development of various intelligent image
sensors. The main ingredient in accelerating the optimization of our model,
particularly the selection of binary convolution kernels, is the Chainer/Cupy
machine learning library offering significant speed-ups for training the output
layer as an extreme-learning machine. Additional training of the output layer
using Keras/Tensorflow is included, as it allows an increase in accuracy.
Results for widely used datasets including MNIST, GTSRB, ORL, VGG show very
good compromise between accuracy and complexity. Particularly, for face
recognition problems a carefully optimized LB-CNN model provides up to 100%
accuracies. Such TinyML solutions are well suited for industrial applications
requiring image recognition with low energy consumption.
