Non-Boolean computing with nanomagnets for computer vision applications

Nature Nanotechnology

Volumn 11, Issue 2, 2016, Pages 177-183

  Bhanja, Sanjukta ^a   Karunaratne, D K ^a   Panchumarthy, Ravi ^a   Rajaram, Srinath ^a   Sarkar, Sudeep ^a  

^a University of South Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STORAGE EQUIPMENT; HAMILTONIANS; NANOMAGNETICS; QUADRATIC PROGRAMMING;

COMPUTER VISION APPLICATIONS; ENERGY MINIMIZATION; NANOMAGNETIC SYSTEMS; NON-VOLATILE MEMORY; QUADRATIC OPTIMIZATION PROBLEMS; STATE REPRESENTATION; TRUE POSITIVE RATES; UNCONVENTIONAL COMPUTING;

COMPUTER VISION;

ARTICLE; COMPUTER ANALYSIS; ENERGY; MAGNETISM; NANOMAGNETISM; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

EID: 84957843943     PISSN: 17483387     EISSN: 17483395     Source Type: Journal    
DOI: 10.1038/nnano.2015.245     Document Type: Article

References (48)

1. Park J., Zha H., & Kasturi R. in Computer Vision-ECCV 2004 (eds Pajdla T., & Matas J.) 390-401 (Springer, 2004
2. Maciel J., & Costeira J. A global solution to sparse correspondence problems. IEEE Trans. Pattern Anal. Mach. Intell. 25, 187-199 (2003
3. Ng A. Y., Jordan M. I., & Weiss Y. On spectral clustering: Analysis, and an algorithm. Adv. Neural Inf. Proc. Syst. 2, 849-856 (2002
4. Shi J., & Malik J. Normalized cuts, and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888-905 (2000
5. Sarkar S., & Soundararajan P. Supervised learning of large perceptual organization: graph spectral partitioning, and learning automata. IEEE Trans. Pattern Anal. Mach. Intell. 22, 504-525 (2000
6. Rota Bulò S., Pelillo M., & Bomze I. M. Graph-based quadratic optimization: A fast evolutionary approach. Comput. Vis. Image Und. 115, 984-995 (2011
7. Levin A., Rav Acha A., & Lischinski D. Spectral matting. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1699-1712 (2008
8. Liu Y., Zhang D., Lu G., & Ma,W.-Y. A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40, 262-282 (2007
9. Iqbal Q., & Aggarwal J. K. Retrieval by classification of images containing large manmade objects using perceptual grouping. Pattern Recogn. 35, 1463-1479 (2002
10. Larsen P., Rawlings J., & Ferrier N. Model-based object recognition to measure crystal size, and shape distributions from in situ video images. Chem. Eng. Sci. 62, 1430-1441 (2007
11. Michaelsen E., Soergel U., & Thoennessen U. Perceptual grouping for automatic detection of man-made structures in high-resolution SAR data. Pattern Recog. Lett. 27, 218-225 (2006
12. Parkin S. S., et al. Giant tunnelling magnetoresistance at room temperature with MgO (100) tunnel barriers. Nature Mater. 3, 862-867 (2004
13. Åkerman J. Toward a universal memory. Science 308, 508-510 (2005
14. Li J., Ndai P., Goel A., Salahuddin S., & Roy K. Design paradigm for robust spin-torque transfer magnetic RAM (STT MRAM) from circuit/architecture perspective. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 18, 1710-1723 (2010
15. Cowburn R. P., & Welland M. E. Room temperature magnetic quantum cellular automata. Science 287, 1466-1468 (2000
16. Imre A., et al. Majority logic gate for magnetic quantum-dot cellular automata. Science 311, 205-208 (2006
17. Alam M., et al. Experimental progress of, and prospects for nanomagnet logic (NML). In Silicon Nanoelectronics Workshop (SNW) 1-2 (2010
18. Karunaratne D. K., & Bhanja S. Study of single layer, and multilayer nanomagnetic logic architectures. J. Appl. Phys. 111, 07A928 (2012
19. Yilmaz Y., & Mazumder P. Nonvolatile nanopipelining logic using multiferroic single-domain nanomagnets. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 21, 1181-1188 (2013
20. Vacca M., et al. Magnetoelastic clock system for nanomagnet logic. IEEE Trans. Nanotechnol. 13, 963-973 (2014
21. Anderson N. G., & Bhanja S. in Field-Coupled Nanocomputing: Paradigms, Progress, and Perspectives (eds Anderson N. G., & Bhanja S.) 8280 (Springer, 2014
22. Behin-Aein B., Datta D., Salahuddin S., & Datta S. Proposal for an all-spin logic device with built-in memory. Nature Nanotech. 5, 266-270 (2010
23. Behin-Aein B. Computing multi-magnet based devices, and methods for solution of optimization problems. US patent 8,698,517 (2014
24. Behin-Aein B., Sarkar A., & Datta S. Modeling circuits with spins, and magnets for all-spin logic. In Proc. European Solid State Dev. Res. Conf. 36-40 (IEEE, 2012
25. Csaba G., & Porod W. Computational study of spin-torque oscillator interactions for non-Boolean computing applications. IEEE Trans. Magn. 49, 4447-4451 (2013
26. Desolneux A., Moisan L., & Morel J.-M. in Seeing, Thinking, and Knowing (ed. Carsetti A.) 71-101 (Springer, 2004
27. Boykov Y., Veksler O., & Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239 (2001
28. METSlib: an open source tabu search metaheuristic framework in modern C++; https://projects.coin-or.org/metslib
29. Kügel A. Diplom-Informatiker Universität Ulm; http://www.maxsat.udl.cat/12/solvers/akmaxsat.pdf
30. IBM ILOG CPLEX Optimizer 12.6.1; http://www-01.ibm.com/software/integration/optimization/cplex-optimizer/
31. Sarkar S., & Bhanja S. Synthesizing energy minimizing quantum-dot cellular automata circuits for vision computing. In 5th IEEE Conf. Nanotechnol. 541-544 (IEEE, 2005
32. Sarkar S., & Bhanja S. Direct quadratic minimization using magnetic field-based computing. In IEEE Int.Workshop on Design, and Test of Nano Dev. Circuits Sys. 31-34 (IEEE, 2008
33. Pulecio J., Bhanja S., & Sarkar S. An experimental demonstration of the viability of energy minimizing computing using nano-magnets. In 11th IEEE Conf. Nanotechnol. 1038-1042 (IEEE, 2011
34. Cox T. F., & Cox M. A. Multidimensional Scaling (CRC Press, 2010
35. Kumari A., Sarkar S., Pulecio J. F., Karunaratne D., & Bhanja S. Study of magnetization state transition in closely spaced nanomagnet two-dimensional array for computation. J. Appl. Phys. 109, 07E513 (2011
36. Scheinfein M. R. LLG micromagnetic simulator; http://llgmicro.home. mindspring.com/(1997
37. Das J., Alam S. M., & Bhanja S. Non-destructive variability tolerant differential read for non-volatile logic. In IEEE 55th Int. Midwest Symp. Circuits Sys. 178-181 (IEEE, 2012
38. Das J., Alam S. M., & Bhanja S. Ultra-low power hybrid CMOS-magnetic logic architecture. IEEE Trans. Circuits Syst. I, Reg. Papers 59, 2008-2016 (2012
39. Lambert C.-H., et al. All-optical control of ferromagnetic thin films, and nanostructures. Science 345, 1337-1340 (2014
40. Liu S., et al. Magnetic-electrical interface for nanomagnet logic. IEEE Trans. Nanotechnol. 10, 757-763 (2011
41. Becherer M., Kiermaier J., Breitkreutz S., Eichwald I., & Schmitt-Landsiedel D. Nanomagnetic logic gate, and an electronic device. US patent 8,872,547 (2014
42. Dong X., et al. Circuit, and microarchitecture evaluation of 3D stacking magnetic RAM (MRAM) as a universal memory replacement. In 45th ACM/IEEE Design Automation Conf. 554-559 (IEEE, 2008
43. Hosaka S., et al. Nano magnetic column arrays fabrication for patterned media in magnetic recording using EB lithography, and ion milling. In IEEE Int. Conf. Electron Dev. Solid-State Circuits 140-143 (IEEE, 2009
44. McGeoch C. C., & Wang C. Experimental evaluation of an adiabiatic quantum system for combinatorial optimization. In Proc. ACM Int. Conf. Computing Frontiers. 23 (ACM, 2013
45. IBM ILOG CPLEX Optimizer performance benchmarks; http://www-01.ibm. com/software/commerce/optimization/cplex-performance/
46. Jog A., et al. in Cache revive: architecting volatile STT-RAM caches for enhanced performance in CMPs. In Proc. 49th Annu. Design Automation Conf. 243-252 (ACM, 2012
47. Kultursay E., Kandemir M., Sivasubramaniam A., & Mutlu O. Evaluating STTRAM as an energy-efficient main memory alternative. In 2013 IEEE Int. Symp. Performance Analysis Sys. Software 256-267 (IEEE, 2013
48. Wang K., Alzate J., & Amiri P. K. Low-power non-volatile spintronic memory: STT-RAM, and beyond. J. Phys. D: Appl. Phys. 46, 074003 (2013