USEFUSE: Utile Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks

Deep neural network (DNN) is an artificial neural network comprised of several layers between input and output layers. They have been widely used in image recognition [1], semantic segmentation [2], medical imaging [3], bioinformatics [4], and signal processing [5] etc. A class of DNN is convolutional neural networks (CNNs) which play a pivotal role in many applications such as computer vision, recognition, object detection, etc. This has been made possible due to the advancements in high performance computing technologies and the availability of cutting-edge compute resources. The use of CNNs with many layers has enabled the swift progress in a number of diverse application domains. CNN designs, inspired by the behavior of optic nerves in human brain, perform data processing in multiple layers of neurons to achieve human brain-like performance in image recognition. This research was supported by Basic Science Research Program funded by the Ministry of Education through the National Research Foundation of Korea (NRF-2020R1I1A3063857). The EDA tool was supported by the IC Design Education Center (IDEC), Korea.