5 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…
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…
DistDNAS: Search Efficient Feature Interactions within 2 Hours
Tunhou Zhang, Wei Wen, Igor Fedorov +8
Search efficiency and serving efficiency are two major axes in building feature interactions and expediting the model development process in recommender systems. On large-scale ben…
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…