1 code implementation • 8 Oct 2021 • Linghao Song, Yuze Chi, Jason Cong
In this work, we present PYXIS, a performance dataset for specialized accelerators on sparse data.
no code implementations • 21 Jul 2020 • Pengcheng Dai, Jianlei Yang, Xucheng Ye, Xingzhou Cheng, Junyu Luo, Linghao Song, Yiran Chen, Weisheng Zhao
In this paper, \textit{SparseTrain} is proposed to accelerate CNN training by fully exploiting the sparsity.
no code implementations • 2 Feb 2019 • Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel Perdue, Thomas E. Potok
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics.
no code implementations • 7 Jan 2019 • Linghao Song, Jiachen Mao, Youwei Zhuo, Xuehai Qian, Hai Li, Yiran Chen
In this paper, inspired by recent work in machine learning systems, we propose a solution HyPar to determine layer-wise parallelism for deep neural network training with an array of DNN accelerators.
1 code implementation • 5 Jun 2018 • Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Hai Li, Yiran Chen
Successful realization of DPatch also illustrates the intrinsic vulnerability of the modern detector architectures to such patch-based adversarial attacks.
no code implementations • 21 Aug 2017 • Linghao Song, Youwei Zhuo, Xuehai Qian, Hai Li, Yiran Chen
GRAPHR gains a speedup of 1. 16x to 4. 12x, and is 3. 67x to 10. 96x more energy efficiency compared to PIM-based architecture.
Distributed, Parallel, and Cluster Computing Hardware Architecture
no code implementations • 7 Jan 2017 • Yandan Wang, Wei Wen, Linghao Song, Hai Li
Brain inspired neuromorphic computing has demonstrated remarkable advantages over traditional von Neumann architecture for its high energy efficiency and parallel data processing.