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Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet
From wearables to powerful smart devices, modern automatic speech recognition (ASR) models run on a variety of edge devices with …
Haichuan Yang, Yuan Shangguan, Dilin Wang, Meng Li, Pierce Chuang, Xiaohui Zhang, Ganesh Venkatesh, Ozlem Kalinli, Vikas Chandra
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
We design deep neural networks (DNNs) and corresponding networks’ splittings to distribute DNNs’ workload to camera sensors …
Xin Dong, Ziyun Li, Meng Li, Zhongnan Qu, Barbara De Salvo, Chiao Liu, Hsiang-Tsung Kung
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training
Designing accurate and efficient vision transformers (ViTs) is a highly important but challenging task. Supernet-based one-shot neural …
Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra
Federated Learning with Non-IID Data
Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model …
Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and …
Meng Li, Liangzhen Lai, Naveen Suda, Vikas Chandra, David Z. Pan