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- research-articleNovember 2022
Flatfish: A Reinforcement Learning Approach for Application-Aware Address Mapping
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCADICS), Volume 41, Issue 11Pages 4758–4770https://doi.org/10.1109/TCAD.2022.3146204The DRAM performance has become a critical bottleneck of modern computing systems. Prior studies have proposed various optimization techniques on address mapping to bridge the gap between real performance and the peak performance. Nevertheless, these ...
- research-articleJune 2022
Hyperscale FPGA-as-a-service architecture for large-scale distributed graph neural network
- Shuangchen Li,
- Dimin Niu,
- Yuhao Wang,
- Wei Han,
- Zhe Zhang,
- Tianchan Guan,
- Yijin Guan,
- Heng Liu,
- Linyong Huang,
- Zhaoyang Du,
- Fei Xue,
- Yuanwei Fang,
- Hongzhong Zheng,
- Yuan Xie
ISCA '22: Proceedings of the 49th Annual International Symposium on Computer ArchitecturePages 946–961https://doi.org/10.1145/3470496.3527439Graph neural network (GNN) is a promising emerging application for link prediction, recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM-GNN), however, the enterprises usually adopt huge graph with large-scale ...
- research-articleDecember 2021
BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
2021 58th ACM/IEEE Design Automation Conference (DAC)Pages 1009–1014https://doi.org/10.1109/DAC18074.2021.9586181In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for analyzing non-euclidean graph data. By applying deep-learning to extract high-level representations from graph structures, GNNs achieve extraordinary accuracy and ...
- research-articleJuly 2020
Crane: Mitigating Accelerator Under-utilization Caused by Sparsity Irregularities in CNNs
IEEE Transactions on Computers (ITCO), Volume 69, Issue 7Pages 931–943https://doi.org/10.1109/TC.2020.2981080Convolutional neural networks (CNNs) have achieved great success in numerous AI applications. To improve inference efficiency of CNNs, researchers have proposed various pruning techniques to reduce both computation intensity and storage overhead. These ...
- research-articleJanuary 2017
FPGA-based accelerator for long short-term memory recurrent neural networks
2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)Pages 629–634https://doi.org/10.1109/ASPDAC.2017.7858394Long Short-Term Memory Recurrent neural networks (LSTM-RNNs) have been widely used for speech recognition, machine translation, scene analysis, etc. Unfortunately, general-purpose processors like CPUs and GPGPUs can not implement LSTM-RNNs efficiently due ...
- research-articleFebruary 2015
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
FPGA '15: Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysPages 161–170https://doi.org/10.1145/2684746.2689060Convolutional neural network (CNN) has been widely employed for image recognition because it can achieve high accuracy by emulating behavior of optic nerves in living creatures. Recently, rapid growth of modern applications based on deep learning ...