Memristor-based approximated computation

Proceedings of the International Symposium on Low Power Electronics and Design

Volumn , Issue , 2013, Pages 242-247

  Li, Boxun ^a   Shan, Yi ^a   Hu, Miao ^b   Wang, Yu ^a   Chen, Yiran ^b   Yang, Huazhong ^a  

^a TSINGHUA UNIVERSITY   (China)

^b UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

approximated computation; memristor; neuromorphic; power efficiency

Indexed keywords

BETTER PERFORMANCE; CONFIGURATION ALGORITHM; DIGITAL IMPLEMENTATION; HARDWARE REALIZATION; MEMRISTOR; NEUROMORPHIC; PHYSICAL REALIZATION; POWER EFFICIENCY;

EFFICIENCY; FEEDBACK; LOW POWER ELECTRONICS; MULTILAYER NEURAL NETWORKS; PASSIVE FILTERS; TUNING;

MEMRISTORS;

EID: 84889601634     PISSN: 15334678     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISLPED.2013.6629302     Document Type: Conference Paper

References (23)

1. DARPA. Power efficiency revolution for embedded computing technologies.
2. NVIDIA TESLA K-SERIES DATASHEET. Kepler family product overview, 2012.
3. Esmaeilzadeh et al. Dark silicon and the end of multicore scaling. In ISCA, pages 365-376. IEEE, 2011.
4. Duygu Kuzum, Rakesh GD Jeyasingh, et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano letters, 12(5):2179-2186, 2011.
5. Jeffrey Dean et al. Large scale distributed deep networks. In Neural Information Processing Systems, 2012.
6. Ngiam et al. On optimization methods for deep learning. In Proceedings of the 28th International Conference on Machine Learning, 2011.
7. Sung Hyun Jo, Ting Chang, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano letters, 10(4):1297-1301, 2010.
8. Jianxing Wang et al. A practical low-power memristor-based analog neural branch predictor. In Proceedings of ISLPED 2013.
9. Miao Hu, Hai Li, et al. Hardware realization of bsb recall function using memristor crossbar arrays. In DAC, pages 498-503, 2012.
10. K. Hornik, M. Stinchcombe, et al. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5):359-366, 1989.
11. Belhadj et al. Continuous real-world inputs can open up alternative accelerator designs. In ISCA. ACM, 2013.
12. Olivier Temam. A defect-tolerant accelerator for emerging highperformance applications. In ISCA, 2012.
13. Esmaeilzadeh et al. Neural acceleration for general-purpose approximate programs. In MICRO, pages 449-460, 2012.
14. Dmitri B Strukov, Gregory S Snider, et al. The missing memristor found. Nature, 453(7191):80-83, 2008.
15. Pino et al. Statistical memristor modeling and case study in neuromorphic computing. In DAC, pages 585-590. ACM, 2012.
16. Yoshifusa Ito. Approximation capability of layered neural networks with sigmoid units on two layers. Neural Computation, 6(6):1233-1243, 1994.
17. Laurene V Fausett. Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall Englewood Cliffs, 1994.
18. G. Khodabandehloo, M. Mirhassani, and M. Ahmadi. Analog implementation of a novel resistive-type sigmoidal neuron. TVLSI, 20(4):750-754, april 2012.
19. Sangho Shin, Kyungmin Kim, and Sung-Mo Kang. Memristor applications for programmable analog ics. Nanotechnology, IEEE Transactions on, 10(2):266-274, march 2011.
20. Wei Yi, Frederick Perner, et al. Feedback write scheme for memristive switching devices. Applied Physics A, 102:973-982, 2011.
21. Jim Mutch and David G. Lowe. Object class recognition and localization using sparse features with limited receptive fields. Int. J. Comput. Vision, 80(1):45-57, October 2008.
22. Ahmed Al Maashri, Michael Debole, et al. Accelerating neuromorphic vision algorithms for recognition. In DAC, DAC '12, pages 579-584, New York, NY, USA, 2012. ACM.
23. Mark Everingham et al. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010.