10 citations · 19 across the 4 of their papers we have counts for
4 papers
AutoRAC: Automated Processing-in-Memory Accelerator Design for Recommender Systems
Feng Cheng, Tunhou Zhang, Junyao Zhang +6
The performance bottleneck of deep-learning-based recommender systems resides in their backbone Deep Neural Networks. By integrating Processing-In-Memory~(PIM) architectures, resea…
NASGEM: Neural Architecture Search via Graph Embedding Method
Hsin-Pai Cheng, Tunhou Zhang, Yixing Zhang +7
Neural Architecture Search (NAS) automates and prospers the design of neural networks. Estimator-based NAS has been proposed recently to model the relationship between architecture…
Accelerating CNN Training by Pruning Activation Gradients
Xucheng Ye, Pengcheng Dai, Junyu Luo +4
Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dy…
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Chang Song, Hsin-Pai Cheng, Huanrui Yang +5
Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this w…