4 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 4 cited
Binary Complex Neural Network Acceleration on FPGA
Hongwu Peng, Shanglin Zhou, Scott Weitze +9
Being able to learn from complex data with phase information is imperative for many signal processing applications. Today' s real-valued deep neural networks (DNNs) have shown effi…
cs.LG2021
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data
Jiandong Mu, Mengdi Wang, Feiwen Zhu +3
Recently, neural network compression schemes like channel pruning have been widely used to reduce the model size and computational complexity of deep neural network (DNN) for appli…
cs.CV2018
AI Matrix - Synthetic Benchmarks for DNN
Wei Wei, Lingjie Xu, Lingling Jin +2
Deep neural network (DNN) architectures, such as convolutional neural networks (CNN), involve heavy computation and require hardware, such as CPU, GPU, and AI accelerators, to prov…