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20172025
most citedRecursive Binary Neural Network Learning Model with 2.28b/Weight Storage Requirement

6 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AR2025★ 1 cited

Enabling Efficient Transaction Processing on CXL-Based Memory Sharing

Zhao Wang, Yiqi Chen, Cong Li +5

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute…

cs.DC2022

Predicting the Output Structure of Sparse Matrix Multiplication with Sampled Compression Ratio

Zhaoyang Du, Yijin Guan, Tianchan Guan +7

Sparse general matrix multiplication (SpGEMM) is a fundamental building block in numerous scientific applications. One critical task of SpGEMM is to compute or predict the structur…

cs.DC2022

OpSparse: a Highly Optimized Framework for Sparse General Matrix Multiplication on GPUs

Zhaoyang Du, Yijin Guan, Tianchan Guan +4

Sparse general matrix multiplication (SpGEMM) is an important and expensive computation primitive in many real-world applications. Due to SpGEMM's inherent irregularity and the vas…

cs.DC2022

Accelerating CPU-Based Sparse General Matrix Multiplication With Binary Row Merging

Zhaoyang Du, Yijin Guan, Tianchan Guan +3

Sparse general matrix multiplication (SpGEMM) is a fundamental building block for many real-world applications. Since SpGEMM is a well-known memory-bounded application with vast an…

cs.NE2017★ 6 cited

Recursive Binary Neural Network Learning Model with 2.28b/Weight Storage Requirement

Tianchan Guan, Xiaoyang Zeng, Mingoo Seok

This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for sensing devices having a limited amount of on-chip data storage such as < 100's k…