Learning separable fixed-point kernels for deep convolutional neural networks

Deep convolutional neural networks have shown outstanding performance in several speech and image recognition tasks. However they demand high computational complexity which limits their deployment in resource limited machines. The proposed work lowers the hardware complexity by constraining the learned convolutional kernels to be separable and also reducing the word-length of these kernels and other weights in the fully connected layers. To compensate for the effect of direct quantization, a retraining scheme that includes filter separation and quantization inside of the adaptation procedure is developed in this work. The filter separation reduces the number of parameters and arithmetic operations by 60% for a 5 × 5 kernel, and the quantization further lowers the precision of storage and arithmetic by more than 80 to 90°% when compared to a floating-point algorithm. Experimental results on MNIST and CIFAR-10 datasets are presented.