Signal Processing - November 2016 - 147
importance of inner-product operations,
this article focuses on an approach by
which this tradeoff can be substantially improved. The first aspect of the
approach is the use of a mixed fixed-/
floating-point representation, by which
the dynamic range is more efficiently
used. The second aspect is a simple optimization enabled by the representation
whereby quantized coefficients can all
be scaled to minimize the overall error
incurred. Filters within an EEG-based
seizure-detection system are used to
demonstrate the approach. Substantial
reduction in the error, computational
energy, and hardware area is observed
as a result of the approach.
Authors
Zhuo Wang (zhuow@princeton.edu)
received his B.S. degree in microelectronics and his M.S. degree from Peking
University, Beijing, China, in 2011 and
2013, respectively. Currently, he is
working toward his Ph.D. degree in the
Department of Electrical Engineering,
Princeton University, New Jersey. His
research focuses on leveraging statistical
lecTure noTeS
ing from the Massachusetts Institute of
Technology, Cambridge, in 2005 and
2009, respectively. Currently, he is an
associate professor of electrical engineering at Princeton University, New Jersey,
where he has been since 2009. His
research focuses on advanced sensing
systems, including low-voltage digital
logic and static random access memories, low-noise analog instrumentation
and data conversion, large-area sensing
systems based on flexible electronics,
and low-energy algorithms for em bedded inference, especially for medical applications.
References
[1] Z. Wang, J. Zhang, and N. Verma, "Reducing
quantization error in low-energy FIR filter accelerators,"
in Proc. IEEE Int. Conf. Acoustics, Speech and Signal
Processing, Brisbane, Australia, 2015.
[2] D. Montgomery, E. Peck, and G. Vining,
Introduction to Linear Regression Analysis. Hoboken,
NJ: Wiley, 2015.
[3] A. Shoeb, "Application of machine learning to epileptic seizure onset detection and treatment," Ph.D. dissertation, Massachusetts Inst. Technol., Cambridge,
MA, 2009.
[4] Z. Wang, J. Zhang, and N. Verma, "Realizing
low-energy classification systems by implementing
matrix multiplication directly within an ADC," IEEE
Trans. Biomed. Circuit. Syst., vol. 9, no. 6, pp. 825-
837, 2015.
SP
(continued from page 140)
Mathematical Optimization Society. His
current research is centered around deriving efficient optimization methods for
large-scale data analysis, with an emphasis on techniques for sparse and lowrank models.
References
approaches such as machine learning for
achieving hardware relaxation in an
algorithmic and architectural level in
resource-constrained platforms such as
embedded sensing systems.
Jintao Zhang (jintao@princeton.edu)
received his B.S. degree in electrical engineering from Purdue University, West
Lafayette, Indiana, in 2012. Currently, he
is working toward his Ph.D. degree at
Princeton University, New Jersey. His
research is focused on energy-efficient
signal analysis (mainly classification and
on chip). His primary research interests
are very-large-scale integration system
design, exploring mixed signal circuit
design by using digital controls to enable
various analog functionalities of the complementary metal-oxide-semiconductor
and, thus, enhancing the system performance with both the benefit from analog
circuits and digital circuits.
Naveen Verma (nverma@princeton.
edu) received his B.A.S. degree in electrical and computer engineering from the
University of British Columbia,
Vancouver, Canada, in 2003, and his M.S.
and Ph.D. degrees in electrical engineer-
[1] P. C. Hansen, J. G. Nagy, and D. P. O'Leary,
Deblurring Images: Matrices, Spectra , and
Filtering. Philadelphia: SIAM, 2006.
[2] A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse prob-
lems," SIAM J. Imaging Sci., vol. 2, no. 1, pp.
183-202, Mar. 2009.
[3] MATLAB and Wavelet Toolbox Release R2015b.
Natick, MA: The MathWorks, Inc., 2015.
[4] S. Mallat, A Wavelet Tour of Signal Processing:
The Sparse Way. London, U.K.: Elsevier, 2009.
[5] G. Strang and T. Nguyen, Wavelets and Filter
Banks. Cambridge, MA: Wellesley-Cambridge,
1996.
[6] A. Weber, "The USC-SIPI image database:
Version 5," USC-SIPI Tech. Report 315, Univ. of
Southern California, Los Angeles, 1993.
[7] S. Rout, "Orthogonal vs. biorthogonal wavelets
for image compression," Ph.D. dissertation, Dept.
Electrical and Computer Engineering Virginia Tech,
Blacksburg, VA, 2003.
IEEE Signal Processing Magazine
|
November 2016
|
[8] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P.
Simoncelli, "Image quality assessment: From error
visibility to structural similarity," IEEE Trans. Image
Process., vol. 13, no. 4, pp. 600-612, 2004.
[9] A. Ahmed, B. Recht, and J. Romberg, "Blind deconvolution using convex programming," IEEE Trans.
Inform. Theory, vol. 60, no. 3, pp. 1711-1732, 2014.
[10] J. F. Claerbout, Earth Soundings Analysis:
Processing Versus Inversion, vol. 6, Cambridge, MA:
Blackwell, 1992.
[11] B. Rideout and E. M. Nosal. Personal communication.
[12] M. Schmidt, "Graphical model structure learning with l1-regularization," Ph.D. dissertation, Dept.
Computer Science, Univ. British Columbia, Vancouver, 2010.
SP
147
Table of Contents for the Digital Edition of Signal Processing - November 2016
Signal Processing - November 2016 - Cover1
Signal Processing - November 2016 - Cover2
Signal Processing - November 2016 - 1
Signal Processing - November 2016 - 2
Signal Processing - November 2016 - 3
Signal Processing - November 2016 - 4
Signal Processing - November 2016 - 5
Signal Processing - November 2016 - 6
Signal Processing - November 2016 - 7
Signal Processing - November 2016 - 8
Signal Processing - November 2016 - 9
Signal Processing - November 2016 - 10
Signal Processing - November 2016 - 11
Signal Processing - November 2016 - 12
Signal Processing - November 2016 - 13
Signal Processing - November 2016 - 14
Signal Processing - November 2016 - 15
Signal Processing - November 2016 - 16
Signal Processing - November 2016 - 17
Signal Processing - November 2016 - 18
Signal Processing - November 2016 - 19
Signal Processing - November 2016 - 20
Signal Processing - November 2016 - 21
Signal Processing - November 2016 - 22
Signal Processing - November 2016 - 23
Signal Processing - November 2016 - 24
Signal Processing - November 2016 - 25
Signal Processing - November 2016 - 26
Signal Processing - November 2016 - 27
Signal Processing - November 2016 - 28
Signal Processing - November 2016 - 29
Signal Processing - November 2016 - 30
Signal Processing - November 2016 - 31
Signal Processing - November 2016 - 32
Signal Processing - November 2016 - 33
Signal Processing - November 2016 - 34
Signal Processing - November 2016 - 35
Signal Processing - November 2016 - 36
Signal Processing - November 2016 - 37
Signal Processing - November 2016 - 38
Signal Processing - November 2016 - 39
Signal Processing - November 2016 - 40
Signal Processing - November 2016 - 41
Signal Processing - November 2016 - 42
Signal Processing - November 2016 - 43
Signal Processing - November 2016 - 44
Signal Processing - November 2016 - 45
Signal Processing - November 2016 - 46
Signal Processing - November 2016 - 47
Signal Processing - November 2016 - 48
Signal Processing - November 2016 - 49
Signal Processing - November 2016 - 50
Signal Processing - November 2016 - 51
Signal Processing - November 2016 - 52
Signal Processing - November 2016 - 53
Signal Processing - November 2016 - 54
Signal Processing - November 2016 - 55
Signal Processing - November 2016 - 56
Signal Processing - November 2016 - 57
Signal Processing - November 2016 - 58
Signal Processing - November 2016 - 59
Signal Processing - November 2016 - 60
Signal Processing - November 2016 - 61
Signal Processing - November 2016 - 62
Signal Processing - November 2016 - 63
Signal Processing - November 2016 - 64
Signal Processing - November 2016 - 65
Signal Processing - November 2016 - 66
Signal Processing - November 2016 - 67
Signal Processing - November 2016 - 68
Signal Processing - November 2016 - 69
Signal Processing - November 2016 - 70
Signal Processing - November 2016 - 71
Signal Processing - November 2016 - 72
Signal Processing - November 2016 - 73
Signal Processing - November 2016 - 74
Signal Processing - November 2016 - 75
Signal Processing - November 2016 - 76
Signal Processing - November 2016 - 77
Signal Processing - November 2016 - 78
Signal Processing - November 2016 - 79
Signal Processing - November 2016 - 80
Signal Processing - November 2016 - 81
Signal Processing - November 2016 - 82
Signal Processing - November 2016 - 83
Signal Processing - November 2016 - 84
Signal Processing - November 2016 - 85
Signal Processing - November 2016 - 86
Signal Processing - November 2016 - 87
Signal Processing - November 2016 - 88
Signal Processing - November 2016 - 89
Signal Processing - November 2016 - 90
Signal Processing - November 2016 - 91
Signal Processing - November 2016 - 92
Signal Processing - November 2016 - 93
Signal Processing - November 2016 - 94
Signal Processing - November 2016 - 95
Signal Processing - November 2016 - 96
Signal Processing - November 2016 - 97
Signal Processing - November 2016 - 98
Signal Processing - November 2016 - 99
Signal Processing - November 2016 - 100
Signal Processing - November 2016 - 101
Signal Processing - November 2016 - 102
Signal Processing - November 2016 - 103
Signal Processing - November 2016 - 104
Signal Processing - November 2016 - 105
Signal Processing - November 2016 - 106
Signal Processing - November 2016 - 107
Signal Processing - November 2016 - 108
Signal Processing - November 2016 - 109
Signal Processing - November 2016 - 110
Signal Processing - November 2016 - 111
Signal Processing - November 2016 - 112
Signal Processing - November 2016 - 113
Signal Processing - November 2016 - 114
Signal Processing - November 2016 - 115
Signal Processing - November 2016 - 116
Signal Processing - November 2016 - 117
Signal Processing - November 2016 - 118
Signal Processing - November 2016 - 119
Signal Processing - November 2016 - 120
Signal Processing - November 2016 - 121
Signal Processing - November 2016 - 122
Signal Processing - November 2016 - 123
Signal Processing - November 2016 - 124
Signal Processing - November 2016 - 125
Signal Processing - November 2016 - 126
Signal Processing - November 2016 - 127
Signal Processing - November 2016 - 128
Signal Processing - November 2016 - 129
Signal Processing - November 2016 - 130
Signal Processing - November 2016 - 131
Signal Processing - November 2016 - 132
Signal Processing - November 2016 - 133
Signal Processing - November 2016 - 134
Signal Processing - November 2016 - 135
Signal Processing - November 2016 - 136
Signal Processing - November 2016 - 137
Signal Processing - November 2016 - 138
Signal Processing - November 2016 - 139
Signal Processing - November 2016 - 140
Signal Processing - November 2016 - 141
Signal Processing - November 2016 - 142
Signal Processing - November 2016 - 143
Signal Processing - November 2016 - 144
Signal Processing - November 2016 - 145
Signal Processing - November 2016 - 146
Signal Processing - November 2016 - 147
Signal Processing - November 2016 - 148
Signal Processing - November 2016 - Cover3
Signal Processing - November 2016 - Cover4
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201809
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201807
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201805
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201803
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_201801
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0917
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0717
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0517
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0317
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0117
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0916
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0716
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0516
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0316
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0116
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0915
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0715
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0515
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0315
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0115
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0914
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0714
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0514
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0314
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0114
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0913
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0713
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0513
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0313
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0113
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0912
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0712
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0512
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0312
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0112
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0911
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0711
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0511
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0311
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0111
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0910
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0710
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0510
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0310
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0110
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0909
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0709
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0509
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0309
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0109
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_1108
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0908
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0708
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0508
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0308
https://www.nxtbook.com/nxtbooks/ieee/signalprocessing_0108
https://www.nxtbookmedia.com