Post on 13-Dec-2015
transcript
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Brandon RumbergBrandon RumbergWest Virginia UniversityWest Virginia Universitybrumberg@mix.wvu.edubrumberg@mix.wvu.edu
Analog Filter BanksAnalog Filter Banks
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Themes of this talk
• Analog VLSI–Analog audio
processing
• Analog filter-bank –Relation to wavelets
–Relation to cochlea
• Computation based on physics of devices
Analog Front-end
FeatureExtraction
FeatureExtraction
FeatureExtraction
Filterbank
A/D
Digital Back-end
Higher-LevelProcessing
A/D
A/D
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Ubiquitous Sensors
• Sensors are extremely:–Small
–Cheap
• Devices are:–Small
–Battery powered
–Always on
• With bench top sensors it wouldn't be an issue
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Analog VLSI
• Relation to traditional analog–Not just op-amps and data converters
–Perform computation
• Standard CMOS–Piggy-back on digital developments
–Weak inversion (sub-threshold)
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Analog VLSI
• Low power–Subthreshold
• Fewer transistors
• Small
• Complex operations based on physics
• Lack of precision–Resolution <=>
dynamic range
–Noise
–Nonlinearity
–Biases
• Lack of programming–Hard to reuse earlier
work
• Difficult to design
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Subthreshold
• Weak inversion–Exponential relationship
–Low currents• 0.1pA <-> 1μA I=I0
WLe
κV gsUT
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Floating Gates
• Nonvolatile memory
• Biasing problem
• Add some reconfigurability–Program bias currents
• Adaptation–Learning
–Neural Nets
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Neuromorphic
• Biologically inspired–Humans great at:
• Perception
• Adaptation
• Efficient
• Historically related to Analog VLSI
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Analog VLSI for Audio ProcessingAnalog VLSI for Audio Processing
Analog Front-end
FeatureExtraction
FeatureExtraction
FeatureExtraction
Filterbank
A/D
Digital Back-end
Higher-LevelProcessing
A/D
A/D
Analog/Digital Synergy•Analog: General Tasks•Digital: Specific Tasks
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Analog Filter Bank
• Array of resonators
• Relation to wavelets–Constant relative
bandwidth
–Similar algorithms?
• Time resolution vs. frequency resolution–Resonance
101
102
103
104
-60
-40
-20
0
Frequency (Hz)
Gai
n (d
B)
out1 out2 out3 out4 out5outn
Vin
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CochleaCochlea
Frequency Decomposition•Position dependent
frequency response•Exponential tap spacing
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Silicon CochleaeSilicon Cochleae
•Cochlea resonators aren't independent
outnout2 out3 out4 out5
Vin
out1
101
102
103
104
-60
-40
-20
0
Frequency (Hz)
Gai
n (d
B)
W W W
W W W
Vin
out1 out2 out3 outn
101
102
103
104
105
-30
-25
-20
-15
-10
-5
0
5
10
15
20
Frequency (Hz)
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Quality Factor
• Resonance
• Possibility to do
adaptive Q
C4
101
102
103
104
105
-60
-50
-40
-30
-20
-10
0
10
Frequency (Hz)
Mag
nitu
de (
dB)
C4 Tuning; Gmratio
= 7 -- 1500
100
102
104
106
108
-100
-80
-60
-40
-20
0
20
Frequency (Hz)
Mag
nitu
de (
dB)
Tow Thomas Tuning
Q=f cBW
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BPF Design
• Struggle for high precision–Limited by noise and
nonlinearities
• Has to work across audio range–3 orders of magnitude change in
frequency
–3 orders of magnitude change in current
• Limited by programming precision and transition to strong inversion
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Filter Bank Questions
• How many taps?
• How much Q?
• Phase?
• Orthogonal
filters?
• Reconstruction?
• What is the output?
101
102
103
104
105
-70
-60
-50
-40
-30
-20
-10
0
10
20
Frequency (Hz)
Mag
nitu
de (
dB)
8 taps; Q = 1.2583; Ripple = 0.81347
101
102
103
104
105
-70
-60
-50
-40
-30
-20
-10
0
10
20
Frequency (Hz)
Mag
nitu
de (
dB)
18 taps; Q = 2.033; Ripple = 0.23548
101
102
103
104
105
-40
-30
-20
-10
0
10
20
30
40
Frequency (Hz)M
agni
tude
(dB
)
8 taps; Q = 2.1536; Ripple = 2.7323
101
102
103
104
105
-50
-40
-30
-20
-10
0
10
20
30
40
Frequency (Hz)
Mag
nitu
de (
dB)
18 taps; Q = 5.6143; Ripple = 2.0548
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Response to Sine
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Two Tones
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Response to Speech
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Response to Pulse
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Speech Spectrogram
Circuit ModelFFT
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Frequency Response
101
102
103
104
105
-80
-70
-60
-50
-40
-30
-20
-10
0
10
Frequency (Hz)
Mag
nitu
de (
dB)
16 taps
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Analog Sub-band Processing
• Peak detector
• Vector-matrix multiplier
• Comparator
• Rectifier
• Differentiator
• Nonlinear– tanh
–sinh
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To do...
• Analytic characterization of analog filter banks
• Full portfolio of sub-band processing structures
• DSP back-ends–Full system
–Algorithms
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?