Designing and training a CIFAR-10 model for memory-constrained devices
The tight memory constraint on LM3S6965 forces us to develop a model with extremely low memory utilization. In fact, the target microcontroller has four times less memory capacity than the Arduino Nano.
Despite this challenging constraint, in this recipe, we will demonstrate the effective deployment of CIFAR-10 image classification on this microcontroller by employing the following convolutional neural network (CNN) with TensorFlow:
Figure 10.1: The model tailored for CIFAR-10 dataset image classification
As you can see from the preceding model architecture, the depthwise separable convolution (DepthSeparableConv2D) layer is the leading operator of the model. This operator, discussed in the upcoming Getting ready section, will make the model compact and accurate.
Getting ready
The network tailored in this recipe takes inspiration from the success of the MobileNet v1 model (https://arxiv...
