no code implementations • 16 Jun 2021 • Geng Yuan, Zhiheng Liao, Xiaolong Ma, Yuxuan Cai, Zhenglun Kong, Xuan Shen, Jingyan Fu, Zhengang Li, Chengming Zhang, Hongwu Peng, Ning Liu, Ao Ren, Jinhui Wang, Yanzhi Wang
More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme.
no code implementations • 16 Apr 2021 • Yu Zhang, Moming Duan, Duo Liu, Li Li, Ao Ren, Xianzhang Chen, Yujuan Tan, Chengliang Wang
Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash.
no code implementations • 15 Apr 2021 • Li Li, Moming Duan, Duo Liu, Yu Zhang, Ao Ren, Xianzhang Chen, Yujuan Tan, Chengliang Wang
In our framework, the server evaluates devices' value of training based on their training loss.
no code implementations • 19 Nov 2019 • Ao Ren, Tao Zhang, Yuhao Wang, Sheng Lin, Peiyan Dong, Yen-Kuang Chen, Yuan Xie, Yanzhi Wang
As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and decoding efficiency.
no code implementations • 22 Jul 2019 • Ruizhe Cai, Ao Ren, Olivia Chen, Ning Liu, Caiwen Ding, Xuehai Qian, Jie Han, Wenhui Luo, Nobuyuki Yoshikawa, Yanzhi Wang
Further, the application of SC has been investigated in DNNs in prior work, and the suitability has been illustrated as SC is more compatible with approximate computations.
1 code implementation • 31 Dec 2018 • Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, Yanzhi Wang
The first part of ADMM-NN is a systematic, joint framework of DNN weight pruning and quantization using ADMM.
no code implementations • 10 May 2018 • Zhe Li, Ji Li, Ao Ren, Caiwen Ding, Jeffrey Draper, Qinru Qiu, Bo Yuan, Yanzhi Wang
Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications.
no code implementations • 28 Mar 2018 • Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, Yanzhi Wang
For FPGA implementations on deep convolutional neural networks (DCNNs), we achieve at least 152X and 72X improvement in performance and energy efficiency, respectively using the SWM-based framework, compared with the baseline of IBM TrueNorth processor under same accuracy constraints using the data set of MNIST, SVHN, and CIFAR-10.
no code implementations • 3 Feb 2018 • Xiaolong Ma, Yi-Peng Zhang, Geng Yuan, Ao Ren, Zhe Li, Jie Han, Jingtong Hu, Yanzhi Wang
However, in these works, the memory design optimization is neglected for weight storage, which will inevitably result in large hardware cost.
no code implementations • 2 Feb 2018 • Ruizhe Cai, Ao Ren, Ning Liu, Caiwen Ding, Luhao Wang, Xuehai Qian, Massoud Pedram, Yanzhi Wang
In this paper, we propose VIBNN, an FPGA-based hardware accelerator design for variational inference on BNNs.
no code implementations • 10 Oct 2017 • Hongjia Li, Tianshu Wei, Ao Ren, Qi Zhu, Yanzhi Wang
The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complicated control problems.
no code implementations • 12 Mar 2017 • Ji Li, Zihao Yuan, Zhe Li, Caiwen Ding, Ao Ren, Qinru Qiu, Jeffrey Draper, Yanzhi Wang
Recently, Deep Convolutional Neural Networks (DCNNs) have made unprecedented progress, achieving the accuracy close to, or even better than human-level perception in various tasks.
no code implementations • 18 Nov 2016 • Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang
Stochastic Computing (SC), which uses bit-stream to represent a number within [-1, 1] by counting the number of ones in the bit-stream, has a high potential for implementing DCNNs with high scalability and ultra-low hardware footprint.