An area and energy efficient design of domain-wall memory-based deep convolutional neural networks using stochastic computing
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Jingtong Hu | Yanzhi Wang | Jie Han | Zhe Li | Yipeng Zhang | Xiaolong Ma | Ao Ren | Geng Yuan | Yanzhi Wang | Jie Han | Zhe Li | Geng Yuan | Xiaolong Ma | Yipeng Zhang | Ao Ren | J. Hu
[1] Steve B. Furber,et al. Scalable energy-efficient, low-latency implementations of trained spiking Deep Belief Networks on SpiNNaker , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[2] Luca Benini,et al. YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights , 2016, 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
[3] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[4] Qinru Qiu,et al. C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs , 2018, FPGA.
[5] Yiran Chen,et al. Memristor Crossbar-Based Neuromorphic Computing System: A Case Study , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[6] Kiyoung Choi,et al. Approximate de-randomizer for stochastic circuits , 2015, 2015 International SoC Design Conference (ISOCC).
[7] Qinru Qiu,et al. Designing reconfigurable large-scale deep learning systems using stochastic computing , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).
[8] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[9] Kaushik Roy,et al. Cache Design with Domain Wall Memory , 2016, IEEE Transactions on Computers.
[10] Tsuyoshi Iwagaki,et al. Compact and accurate stochastic circuits with shared random number sources , 2014, 2014 IEEE 32nd International Conference on Computer Design (ICCD).
[11] Kiyoung Choi,et al. Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks , 2016, 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC).
[12] Qinru Qiu,et al. SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing , 2016, ASPLOS.
[13] K. Roy,et al. Numerical analysis of domain wall propagation for dense memory arrays , 2011, 2011 International Electron Devices Meeting.
[14] P. Szolgay,et al. Analysis of a GPU based CNN implementation , 2012, 2012 13th International Workshop on Cellular Nanoscale Networks and their Applications.
[15] Hai Helen Li,et al. Spintronic Memristor Through Spin-Torque-Induced Magnetization Motion , 2009, IEEE Electron Device Letters.
[16] Massoud Pedram,et al. FFT-based deep learning deployment in embedded systems , 2017, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[17] Seyedhamidreza Motaman,et al. Domain Wall Memory-Layout, Circuit and Synergistic Systems , 2015, IEEE Transactions on Nanotechnology.
[18] Ji Li,et al. Softmax Regression Design for Stochastic Computing Based Deep Convolutional Neural Networks , 2017, ACM Great Lakes Symposium on VLSI.
[19] Tieniu Tan,et al. A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[20] Wolfram Burgard,et al. Learning driving styles for autonomous vehicles from demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[21] Noel E. O'Connor,et al. An Efficient Hardware Architecture for a Neural Network Activation Function Generator , 2006, ISNN.
[22] Qinru Qiu,et al. Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework , 2018, AAAI.
[23] Kiyoung Choi,et al. An energy-efficient random number generator for stochastic circuits , 2016, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC).
[24] S. Parkin,et al. Magnetic Domain-Wall Racetrack Memory , 2008, Science.
[25] Ji Li,et al. Towards acceleration of deep convolutional neural networks using stochastic computing , 2017, 2017 22nd Asia and South Pacific Design Automation Conference (ASP-DAC).
[26] Stefano Mattoccia,et al. A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning , 2016, 2016 IEEE Symposium on Computers and Communication (ISCC).
[27] Yiorgos Makris,et al. A dual-mode weight storage analog neural network platform for on-chip applications , 2012, 2012 IEEE International Symposium on Circuits and Systems.
[28] Chao Wang,et al. CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-Circulant Weight Matrices , 2017, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[29] Shahin Nazarian,et al. Normalization and dropout for stochastic computing-based deep convolutional neural networks , 2019, Integr..
[30] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[31] Brian R. Gaines,et al. Stochastic computing , 1967, AFIPS '67 (Spring).
[32] Jason Cong,et al. Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks , 2015, FPGA.
[33] Honglak Lee,et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.
[34] Ji Li,et al. Hardware-driven nonlinear activation for stochastic computing based deep convolutional neural networks , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[35] Claus Nebauer,et al. Evaluation of convolutional neural networks for visual recognition , 1998, IEEE Trans. Neural Networks.
[36] Luca Benini,et al. YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration , 2016, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[37] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..