Efficient AI

AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
Neural architecture search (NAS) has shown great promise in designing state-of-the-art (SOTA) models that are both accurate and …
Dilin Wang, 李萌, Chengyue Gong, Vikas Chandra
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
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