11 citations · 13 across the 3 of their papers we have counts for
3 papers
cs.IR2024
Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems
Qinyi Luo, Penghan Wang, Wei Zhang +8
Huge embedding tables in modern deep learning recommender models (DLRM) require prohibitively large memory during training and inference. This paper proposes FIITED, a system to au…
cs.LG2019★ 2 cited
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement
Qing Yang, Jiachen Mao, Zuoguan Wang +1
To improve the execution speed and efficiency of neural networks in embedded systems, it is crucial to decrease the model size and computational complexity. In addition to conventi…
cs.DC2019★ 11 cited
HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
Linghao Song, Jiachen Mao, Youwei Zhuo +3
With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have been widely used in many domains. To achieve high performance and energy efficiency, hard…