2 citations · 2 across the 9 of their papers we have counts for
9 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…
Towards Automated Model Design on Recommender Systems
Tunhou Zhang, Dehua Cheng, Yuchen He +10
The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural network…
qGDP: Quantum Legalization and Detailed Placement for Superconducting Quantum Computers
Junyao Zhang, Guanglei Zhou, Feng Cheng +6
Noisy Intermediate-Scale Quantum (NISQ) computers are currently limited by their qubit numbers, which hampers progress towards fault-tolerant quantum computing. A major challenge i…
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS
Hao Fu, Tunhou Zhang, Hai Li +1
Recent advances in Out-of-Distribution (OOD) Detection is the driving force behind safe and reliable deployment of Convolutional Neural Networks (CNNs) in real world applications.…
CSCO: Connectivity Search of Convolutional Operators
Tunhou Zhang, Shiyu Li, Hsin-Pai Cheng +3
Exploring dense connectivity of convolutional operators establishes critical "synapses" to communicate feature vectors from different levels and enriches the set of transformations…
Peeking Behind the Curtains of Residual Learning
Tunhou Zhang, Feng Yan, Hai Li +1
The utilization of residual learning has become widespread in deep and scalable neural nets. However, the fundamental principles that contribute to the success of residual learning…