27 citations · 32 across the 4 of their papers we have counts for
4 papers
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators
Bingqian Lu, Zheyu Yan, Yiyu Shi +1
In view of the performance limitations of fully-decoupled designs for neural architectures and accelerators, hardware-software co-design has been emerging to fully reap the benefit…
One Proxy Device Is Enough for Hardware-Aware Neural Architecture Search
Bingqian Lu, Jianyi Yang, Weiwen Jiang +2
Convolutional neural networks (CNNs) are used in numerous real-world applications such as vision-based autonomous driving and video content analysis. To run CNN inference on variou…
Scaling Up Deep Neural Network Optimization for Edge Inference
Bingqian Lu, Jianyi Yang, Shaolei Ren
Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. To run DNN inference directly…
A Note on Latency Variability of Deep Neural Networks for Mobile Inference
Luting Yang, Bingqian Lu, Shaolei Ren
Running deep neural network (DNN) inference on mobile devices, i.e., mobile inference, has become a growing trend, making inference less dependent on network connections and keepin…