23 citations · 36 across the 6 of their papers we have counts for
6 papers
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
Guan Shen, Jieru Zhao, Zeke Wang +5
Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost…
P4SGD: Programmable Switch Enhanced Model-Parallel Training on Generalized Linear Models on Distributed FPGAs
Hongjing Huang, Yingtao Li, Jie Sun +5
Generalized linear models (GLMs) are a widely utilized family of machine learning models in real-world applications. As data size increases, it is essential to perform efficient di…
ScalaBFS: A Scalable BFS Accelerator on HBM-Enhanced FPGAs
Kexin Li, Chenhao Liu, Zhiyuan Shao +5
High Bandwidth Memory (HBM) provides massive aggregated memory bandwidth by exposing multiple memory channels to the processing units. To achieve high performance, an accelerator b…
Optimizing Memory Performance of Xilinx FPGAs under Vitis
Ruoshi Li, Hongjing Huang, Zeke Wang +3
Plenty of research efforts have been devoted to FPGA-based acceleration, due to its low latency and high energy efficiency. However, using the original low-level hardware descripti…
Benchmarking High Bandwidth Memory on FPGAs
Zeke Wang, Hongjing Huang, Jie Zhang +1
FPGAs are starting to be enhanced with High Bandwidth Memory (HBM) as a way to reduce the memory bandwidth bottleneck encountered in some applications and to give the FPGA more cap…
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)
Zeke Wang, Kaan Kara, Hantian Zhang +3
Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest…