Efficient AI

DNA: Differentiable Network-Accelerator Co-Search
Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered …
Yongan Zhang, Yonggan Fu, Weiwen Jiang, Chaojian Li, Haoran You, 李萌, Vikas Chandra, Yingyan Lin
论文
AlphaNet: Improved Training of Supernets with Alpha-Divergence
Weight-sharing neural architecture search (NAS) is an effective technique for automating efficient neural architecture design. …
Dilin Wang, Chengyue Gong, 李萌, Qiang Liu, Vikas Chandra
论文
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
An attentive sampling strategy that improves weight-sharing neural architecture search across a broad efficiency range.
Dilin Wang, 李萌, Chengyue Gong, Vikas Chandra
论文
Improving efficiency in neural network accelerator using operands hamming distance optimization
Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric. The …
李萌, Yilei Li, Vikas Chandra
论文
Co-exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks
Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and …
Lei Yang, Zheyu Yan, 李萌, Hyoukjun Kwon, Liangzhen Lai, Tushar Krishna, Vikas Chandra, Weiwen Jiang, Yiyu Shi
论文