Faster Convolutional Neural Networks in Low Density FPGAs Using Block Pruning

Abstract

Convolutional Neural Networks (CNNs) are achieving promising results in several computer vision applications. Running these models is computationally very intensive and needs a large amount of memory to store weights and activations. Therefore, CNN typically run on high performance platforms. However, the classification capabilities of CNNs are very useful in many applications running in embedded platforms close to data production since it avoids data communication for cloud processing and permits real-time decisions turning these systems into smart embedded systems. In this paper, we improve the inference of large CNN in low density FPGAs using pruning. We propose block pruning and apply it to LiteCNN, an architecture for CNN inference that achieves high performance in low density FPGAs. With the proposed LiteCNN optimizations, we have an architecture for CNN inference with an average performance of 275 GOPs for 8-bit data in a XC7Z020 FPGA. With our proposal, it is possible to infer an image in AlexNet in 5.1 ms in a ZYNQ7020 and in 13.2 ms in a ZYNQ7010 with only 2.4% accuracy degradation.