Chao Wang

TL;DR: This paper designs deep learning accelerator unit (DLAU), which is a scalable accelerator architecture for large-scale deep learning networks using field-programmable gate array (FPGA) as the hardware prototype and employs three pipelined processing units to improve the throughput.

TL;DR: A software-based coarse-grained pruning technique, together with local quantization, significantly reduces the size of indexes and improves the network compression ratio and a hardware accelerator is designed to address the remaining irregularity of sparse synapses and neurons efficiently.

TL;DR: A new architecture for FPGA-based CNN accelerator that maps all the layers to their own on-chip units and working concurrently as a pipeline is proposed, which can achieve maximum resource utilization as well as optimal computational efficiency.

TL;DR: The experimental results suggest that the WGAN-based oversampling technique can synthesize helpful samples for the minority classes to assist the training of the CNN model and to boost the fine-grained classification performance better than the conventional data augmentation method and the two schemes of the GAN and DCGAN techniques do.

TL;DR: Experimental results on the prototype system demonstrate NeverStop can efficiently facilitate researchers to reduce the average waiting time for vehicles, and a genetic algorithm illustrating how theaverage waiting time is derived is presented.