6 citations · 11 across the 6 of their papers we have counts for
6 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…
A Survey: Collaborative Hardware and Software Design in the Era of Large Language Models
Cong Guo, Feng Cheng, Zhixu Du +21
The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language…
FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
Minxue Tang, Yitu Wang, Jingyang Zhang +5
Federated Adversarial Training (FAT) can supplement robustness against adversarial examples to Federated Learning (FL), promoting a meaningful step toward trustworthy AI. However,…
ICGMM: CXL-enabled Memory Expansion with Intelligent Caching Using Gaussian Mixture Model
Hanqiu Chen, Yitu Wang, Luis Vitorio Cargnini +7
Compute Express Link (CXL) emerges as a solution for wide gap between computational speed and data communication rates among host and multiple devices. It fosters a unified and coh…
NDSEARCH: Accelerating Graph-Traversal-Based Approximate Nearest Neighbor Search through Near Data Processing
Yitu Wang, Shiyu Li, Qilin Zheng +5
Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector database and many data center applications, such as person re-identification and recommendation s…
FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning
Minxue Tang, Xuefei Ning, Yitu Wang +4
Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramaticall…