A mixed-precision binary neural network architecture for touch modality classification

Binary Neural Networks (BNN) have been proposed to address the computational complexity and memory requirements of Convolutional Neural Networks (CNN). However, in most of the applications, BNNs suffer from severe accuracy loss due to the 1-bit quantization. In this paper, a Mixed-Precision Binary Weight Network (MP-BWN) is proposed as a compromise between CNN and BNN. Compared to traditional binary networks, MP-BWN offers better performance with an acceptable increase in the network size. MP-BWN achieves up to 99% reduction in both the number of operations and the network size compared to similar state-of-the-art solutions. When validated on a touch modality classification problem, the MP-BWN surpassed similar existing solutions by achieving a classification accuracy of 77.8%.