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View all- Mondal ASrivastava A(2019)Energy-efficient Design of MTJ-based Neural Networks with Stochastic ComputingACM Journal on Emerging Technologies in Computing Systems10.1145/335962216:1(1-27)Online publication date: 15-Oct-2019
Spintronics based stochastic computing for efficient bayesian inference system
Authors: 
Xiaotao Jia, Jianlei Yang, Zhaohao Wang, Yiran Chen, Hai (Helen) Li, 
Weisheng ZhaoAuthors Info & Claims
Xiaotao Jia, Jianlei Yang, Zhaohao Wang, Yiran Chen, Hai (Helen) Li, Weisheng ZhaoAuthors Info & ClaimsPages 580 - 585
Abstract
Bayesian inference is an effective approach for solving statistical learning problems especially with uncertainty and incompleteness. However, inference efficiencies are physically limited by the bottlenecks of conventional computing platforms. In this paper, an emerging Bayesian inference system is proposed by exploiting spintronics based stochastic computing. A stochastic bitstream generator is realized as the kernel components by leveraging the inherent randomness of spintronics devices. The proposed system is evaluated by typical applications of data fusion and Bayesian belief networks. Simulation results indicate that the proposed approach could achieve significant improvement on inference efficiencies in terms of power consumption and inference speed.
References
[1]
P. Pinheiro and P. Lima, "Bayesian sensor fusion for cooperative object localization and world modeling," in CIAS. Citeseer, 2004.
[2]
N. Cruz-Ramírez, H. G. Acosta-Mesa, H. Carrillo-Calvet, L. A. Nava-Fernandez, and R. E. Barrientos-Martínez, "Diagnosis of breast cancer using bayesian networks: A case study," Computers in Biology and Medicine, vol. 37, no. 11, pp. 1553--1564, 2007.
[3]
Y. Gal, R. Islam, and Z. Ghahramani, "Deep bayesian active learning with image data," arXiv preprint arXiv:1703 02910, 2017.
[4]
C. S. Thakur, S. Afshar, R. M. Wang, T. J. Hamilton, J. Tapson, and A. Van Schaik, "Bayesian estimation and inference using stochastic electronics," Frontiers in neuroscience, vol. 10, 2016.
[5]
A. Alaghi and J. P. Hayes, "Survey of stochastic computing," TECS, vol. 12, no. 2s, p. 92, 2013.
[6]
A. F. Vincent, N. Locatelli, J.-O. Klein, W. S. Zhao, S. Galdin-Retailleau, and D. Querlioz, "Analytical macrospin modeling of the stochastic switching time of spin-transfer torque devices," IEEE Transactions on Electron Devices, vol. 62, no. 1, pp. 164--170, 2015.
[7]
J. Grollier, D. Querlioz, and M. D. Stiles, "Spintronic nanodevices for bioinspired computing," Proceedings of the IEEE, vol. 104, no. 10, pp. 2024--2039, 2016.
[8]
M. Lin, I. Lebedev, and J. Wawrzynek, "High-throughput bayesian computing machine with reconfigurable hardware," in FPGA. ACM, 2010, pp. 73--82.
[9]
P. Mroszczyk and P. Dudek, "The accuracy and scalability of continuous-time bayesian inference in analogue cmos circuits," in ISCAS. IEEE, 2014, pp. 1576--1579.
[10]
L. A. de Barros Naviner, H. Cai, Y. Wang, W. Zhao, and A. B. Dhia, "Stochastic computation with spin torque transfer magnetic tunnel junction," in NEWCAS. IEEE, 2015, pp. 1--4.
[11]
Y. Wang, H. Cai, L. A. Naviner, J.-O. Klein, J. Yang, and W. Zhao, "A novel circuit design of true random number generator using magnetic tunnel junction," in NANOARCH. IEEE, 2016, pp. 123--128.
[12]
S. Wang, S. Pal, T. Li, A. Pan, C. Grezes, P. Khalili-Amiri, K. L. Wang, and P. Gupta, "Hybrid vc-mtj/cmos non-volatile stochastic logic for efficient computing," in DATE. IEEE, 2017, pp. 1438--1443.
[13]
J. Von Neumann, "Probabilistic logics and the synthesis of reliable organisms from unreliable components," Automata studies, vol. 34, pp. 43--98, 1956.
[14]
P. Jeavons, D. A. Cohen, and J. Shawe-Taylor, "Generating binary sequences for stochastic computing," IEEE Transactions on Information Theory, vol. 40, no. 3, pp. 716--720, 1994.
[15]
P. K. Gupta and R. Kumaresan, "Binary multiplication with pn sequences," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 4, pp. 603--606, 1988.
[16]
T. Devolder, J. Hayakawa, K. Ito, H. Takahashi, S. Ikeda, P. Crozat, N. Zerounian, J.-V. Kim, C. Chappert, and H. Ohno, "Single-shot time-resolved measurements of nanosecond-scale spin-transfer induced switching: Stochastic versus deterministic aspects," Physical review letters, vol. 100, no. 5, p. 057206, 2008.
[17]
M. Marins de Castro, R. Sousa, S. Bandiera et al., "Precessional spin-transfer switching in a magnetic tunnel junction with a synthetic antiferromagnetic perpendicular polarizer," Journal of Applied Physics, vol. 111, no. 7, p. 07C912, 2012.
[18]
W. Zhao, C. Chappert, V. Javerliac, and J.-P. Noziere, "High speed, high stability and low power sensing amplifier for mtj/cmos hybrid logic circuits," IEEE Transactions on Magnetics, vol. 45, no. 10, pp. 3784--3787, 2009.
[19]
Y. Wang, Y. Zhang, E. Deng, J.-O. Klein, L. A. Naviner, and W. Zhao, "Compact model of magnetic tunnel junction with stochastic spin transfer torque switching for reliability analyses," Microelectronics Reliability, vol. 54, no. 9, pp. 1774--1778, 2014.
[20]
A. Coninx, P. Bessière, E. Mazer, J. Droulez, R. Laurent, M. A. Aslam, and J. Lobo, "Bayesian sensor fusion with fast and low power stochastic circuits," in ICRC. IEEE, 2016, pp. 1--8.
[21]
(2017) Pythonic bayesian belief network framework. https://github.com/eBay/bayesian-belief-networks.
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- IEEE Circuits and Systems Society
- SIGDA: ACM Special Interest Group on Design Automation
- IEEE Council on Electronic Design Automation (CEDA)
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Published: 22 January 2018
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ASPDAC '18: 23rd Asia and South Pacific Design Automation Conference
January 22 - 25, 2018
Jeju, Republic of Korea
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View all- Mondal ASrivastava A(2019)Energy-efficient Design of MTJ-based Neural Networks with Stochastic ComputingACM Journal on Emerging Technologies in Computing Systems10.1145/335962216:1(1-27)Online publication date: 15-Oct-2019
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