65 citations · 218 across the 26 of their papers we have counts for
6 papers · 1 filter
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Yonggan Fu, Haoran You, Yang Zhao +5
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such…
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
Yonggan Fu, Zhongzhi Yu, Yongan Zhang +1
AlphaGo's astonishing performance has ignited an explosive interest in developing deep reinforcement learning (DRL) for numerous real-world applications, such as intelligent roboti…
DNA: Differentiable Network-Accelerator Co-Search
Yongan Zhang, Yonggan Fu, Weiwen Jiang +5
Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered intelligence into numerous applications. Jointly opti…
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
Yonggan Fu, Wuyang Chen, Haotao Wang +3
The compression of Generative Adversarial Networks (GANs) has lately drawn attention, due to the increasing demand for deploying GANs into mobile devices for numerous applications…
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
Yang Zhao, Xiaohan Chen, Yue Wang +6
We present SmartExchange, an algorithm-hardware co-design framework to trade higher-cost memory storage/access for lower-cost computation, for energy-efficient inference of deep ne…
Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference
Jianghao Shen, Yonggan Fu, Yue Wang +3
While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing…