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Qinru Qiu
Researcher at Syracuse University
Publications - 192
Citations - 5419
Qinru Qiu is an academic researcher from Syracuse University. The author has contributed to research in topics: Neuromorphic engineering & Spiking neural network. The author has an hindex of 37, co-authored 188 publications receiving 4420 citations. Previous affiliations of Qinru Qiu include Binghamton University & State University of New York System.
Papers
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Proceedings ArticleDOI
CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices
Caiwen Ding,Siyu Liao,Yanzhi Wang,Zhe Li,Ning Liu,Youwei Zhuo,Chao Wang,Xuehai Qian,Yu Bai,Geng Yuan,Xiaolong Ma,Yipeng Zhang,Jian Tang,Qinru Qiu,Xue Lin,Bo Yuan +15 more
TL;DR: The CirCNN architecture is proposed, a universal DNN inference engine that can be implemented in various hardware/software platforms with configurable network architecture (e.g., layer type, size, scales, etc) and FFT can be used as the key computing kernel which ensures universal and small-footprint implementations.
Proceedings ArticleDOI
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
TL;DR: The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem and the proposed framework can achieve the best trade-off between latency and power/energy consumption in a server cluster.
Journal ArticleDOI
Reinforcement learning with analogue memristor arrays
Zhongrui Wang,Can Li,Wenhao Song,Mingyi Rao,Daniel Belkin,Yunning Li,Peng Yan,Hao Jiang,Peng Lin,Miao Hu,John Paul Strachan,Ning Ge,Mark Barnell,Qing Wu,Andrew G. Barto,Qinru Qiu,R. Stanley Williams,Qiangfei Xia,Jianhua Yang +18 more
TL;DR: An experimental demonstration of reinforcement learning on a three-layer 1-transistor 1-memristor (1T1R) network using a modified learning algorithm tailored for the authors' hybrid analogue–digital platform, which has the potential to achieve a significant boost in speed and energy efficiency.
Proceedings ArticleDOI
Dynamic power management based on continuous-time Markov decision processes
Qinru Qiu,Massoud Pedram +1 more
TL;DR: A continuous-time, controllable Markov process model of a power-managed system that captures dependencies between the service queue and service provider status and the resulting power management policy is asynchronous, hence it is more power-efficient and more useful in practice.
Proceedings ArticleDOI
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
TL;DR: C-LSTM as discussed by the authors employs block-circulant instead of sparse matrices to compress weight matrices and reduces the storage requirement from $\mathcalO (k^2)$ to $\MathcalO(k)$.