Low power sparse approximation on reconfigurable analog hardware

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Volumn 2, Issue 3, 2012, Pages 530-541

  Shapero, Samuel ^a   Charles, Adam S ^a   Rozell, Christopher J ^a   Hasler, Paul ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Compressed sensing; convex optimization; field programmable analog arrays; Hopfield neural networks

Indexed keywords

ACTIVE CIRCUITS; ANALOG HARDWARE; ANALOG SYSTEMS; COMPETITIVE ALGORITHMS; COMPRESSIVE SENSING; DIGITAL OPTIMIZATION; DIGITAL SOLUTIONS; FIELD PROGRAMMABLE ANALOG ARRAY; FLOATING GATES; LOW POWER; NON-LINEAR OPTIMIZATION PROBLEMS; OVER-COMPLETE; PROBLEM SIZE; SCALABLE SYSTEMS; SCALING TRENDS; SIGNAL AND IMAGE PROCESSING; SPARSE APPROXIMATIONS; SUB-THRESHOLD CURRENT; VECTOR-MATRIX MULTIPLIERS;

APPROXIMATION ALGORITHMS; CONVEX OPTIMIZATION; HOPFIELD NEURAL NETWORKS; IMAGE PROCESSING; LIFE CYCLE; RECONFIGURABLE HARDWARE; SIGNAL RECONSTRUCTION;

PROBLEM SOLVING;

EID: 84870987359     PISSN: 21563357     EISSN: None     Source Type: Journal    
DOI: 10.1109/JETCAS.2012.2214615     Document Type: Article

References (32)

1. C. Rozell and P. Garrigues, "Analog sparse approximation for compressed sensing recovery," in Proc. 2010 ASILOMAR Conf. Signals, Syst. Comput., 2010, pp. 822-826.
2. S. Shapero, C. Rozell, A. Balavoine, and P. Hasler, "A scalable implementation of sparse approximation on a field programmable analog array," in IEEE Biomed. Circuits Syst. Conf., 2011, pp. 141-144.
3. M. Elad, M. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," IEEE Proc., vol. 98, no. 6, pp. 972-982, Apr. 2010.
4. J. Wright, Y.Ma, J.Mairal, G. Sapiro, T. Huang, and S. Yan, "Sparse representation for computer vision and pattern recognition," Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, Jun. 2010.
5. E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction fromhighly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006. (Pubitemid 43174093)
6. E. J. Candès and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, Mar. 2008.
7. B. Olshausen and D. Field, "Emergence of simple-cell receptive field properties by learning in a sparse code for natural images," Nature, vol. 381, pp. 607-609, 1996. (Pubitemid 26177476)
8. J. Mattingley and S. Boyd, "Real-time convex optimization in signal processing," IEEE Signal Process. Mag., vol. 27, no. 3, pp. 50-61, May 2010.
9. S. S. Vasanawala, M. T. Alley, B. A. Hargreaves, R. A. Barth, J. M. Pauly, andM. Lustig, "Improved pediatricMR imaging with compressive sensing," Radiology, vol. 256, pp. 607-616, Aug. 2010.
10. W. Bajwa, J. Haupt, A. Sayeed, and R. Nowak, "Compressed channel sensing: A new approach to estimating sparse multipath channels," Proc. IEEE, vol. 98, no. 6, pp. 1058-1076, Jun. 2010.
11. C. Rozell, D. Johnson, R. Baraniuk, and B. Olshausen, "Sparse coding via thresholding and local competition in neural circuits," Neural Computat., vol. 20, no. 10, pp. 2526-2563, Oct. 2008.
12. A. Balavoine, J. Romberg, and C. Rozell, "Convergence and rate analysis of neural networks for sparse approximation," IEEE Trans. Neural Networks Learn. Syst., vol. 23, no. 9, pp. 1377-1389, Sep. 2012.
13. T. Hall, C. Twigg, P. Hasler, and D. Anderson, "Developing largescale field-programmable analog arrays," in Proc. 18th Int. Parallel Distributed Process. Symp., Apr. 2004, p. 142.
14. B. Olshausen and M. Lewicki et al., "Sparse codes and spikes," Probabilistic Models Brain: Percept. Neural Function, pp. 257-272, 2001.
15. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, 2001. (Pubitemid 32406896)
16. C. Schlottmann and P. Hasler, "A highly dense, lowpower, programmable analog vector-matrix multiplier: The FPAA implementation," IEEE J. Emerg. Sel. Topics Circuits Syst., vol. 1, no. 3, pp. 1-9, Sep. 2011.
17. C. Schlottman, S. Shapero, S. Nease, and P. Hasler, "A digitally enhanced dynamically reconfigurable analog platform for low-power signal processing," IEEE J. Solid-State Circuits, vol. 47, no. 9, pp. 2174-2184, Sep. 2012.
18. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proc. Nat. Acad. Sci., vol. 79, no. 8, pp. 2554-2558, 1982.
19. T. Serrano-Gotarredona, B. Linares-Barranco, and A. Andreou, "Very wide range tunable CMOS/bipolar current mirrors with voltage clamped input," IEEE Trans. Circuits Syst. I: Fundamental Theory Appl., vol. 46, no. 11, pp. 1398-1407, Nov. 1999. (Pubitemid 30519701)
20. S. Shapero and P. Hasler, "Precise programming and mismatch compensation for low power analog computation on an FPAA," IEEE Trans. Circuits Syst. I, 2012, to be published.
21. C. C. Enz, F. Krummenacher, and E. A. Vittoz, "An analytical mos transistor model valid in all regions of operation and dedicated to low-voltage and low-current applications," Analog Integrated Circuits Signal Process., vol. 8, pp. 83-114, 1995.
22. S. Koziol, C. Schlottmann, A. Basu, S. Brink, C. Petre, B. Degnan, S. Ramakrishnan, P. Hasler, and A. Balavoine, "Hardware and software infrastructure for a family of floating-gate based FPAAs," in IEEE Int. Symp. Circuits Syst., May 2010, pp. 2794-2797.
23. C. Schlottmann, C. Petre, and P. Hasler, "A high-level simulink-based tool for FPAA configuration," IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 20, no. 1, pp. 10-18, Jan. 2012.
24. F. Baskaya, D. Anderson, P. Hasler, and S. K. Lim, "A generic reconfigurable array specification and programming environment (GRASPER)," in Circuit Theory Design, Eur. Conf., 2009, pp. 619-622.
25. S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, "An interior-point method for large scale l1-regularized least squares," IEEE J. Sel. Topics Signal Process., vol. 1, no. 4, pp. 606-617, Dec. 2007. (Pubitemid 351266626)
26. M. A. T. Figueiredo, R. D. Nowak, and S. J.Wright, "Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems," IEEE J. Sel. Topics Signal Process., pp. 586-586-597, Dec. 2007.
27. E. T. Hale, W. Yin, and Y. Zhang, "Fixed-point continuation for l1-minimization:Methodology and convergence," SIAM J. Optimizat., vol. 19, no. 3, pp. 1107-1130, 2008.
28. D. Donoho and J. Tanner, "Sparse nonnegative solution of underdetermined linear equations by linear programming," Proc. Nat. Acad. Sci. USA, vol. 102, no. 27, p. 9446, 2005.
29. A. Borghi, J.Darbon, S. Peyronnet, T. F. Chan, and S. Osher, "A simple compressive sensing algorithm for parallel many-core architectures," J. Signal Process. Syst., 2012, to be published.
30. M. Andrecut, "Fast GPU implementation of sparse signal recovery fromrandom projections," Eng. Lett., vol. 17, no. 3, pp. 151-158, 2009.
31. A. Charles, P. Garrigues, and C. Rozell, "A common network architecture efficiently implements a variety of sparsity-based inference problems," Neural Computat., 2012, to be published.
32. M. Rehm and F. Sommer, "A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields," J. Computat. Neurosci., vol. 22, pp. 135-146, 2007. (Pubitemid 46277345)