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TI Information – Selective Disclosure
IEEE Santa Clara ComSoc/CAS Weekend Workshop – Event-based analog sensingTheodore Yu
Texas Instruments – Kilby Labs, Silicon Valley Labs
September 29, 2012
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TI Information – Selective Disclosure
Living in an analog world
• The world is analog– Many different levels to sense
• Sight, sound, touch, taste, smell– Analog interfaces are uniquely suited for each environment
• Increasingly, we turn to machines to help interpret the world for us– Interface through sensors and actuators with computation being
performed in digital machines• e.g. microprocessors, cellphones, CPUs, etc.
– Digital computation is robust, easily configurable, and widespread
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TI Information – Selective Disclosure
Analog-digital interface
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• The placement of the boundary between analog and digital is flexible– But transitions are expensive
• All-digital approach: send raw sensor data to digital domain– Places the burden upon the analog-digital interconnect and digital processing power consumption
• All-analog approach: all-analog signal processing – Often highly task specific which increases development time and reduces generalization to other applications
A D
A D
-Mostly digital• Analog world is directly
sampled into the digital domain
– e.g. all-digital implementations
-Mostly analog• Analog world is processed
and interpreted in analog– e.g. traditional analog
implementations
TI Information – Selective Disclosure
Analog-digital interface – smart sensors
• The placement of the boundary between analog and digital is flexible– But transitions are expensive– Smart sensors and actuators
• Learning and interpretation of analog information• Adaptation in analog sensor and actuator operation
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A D
A D
-Mostly digital• Analog world is directly
sampled into the digital domain
– e.g. all-digital implementations
-Mostly analog• Analog world is processed
and interpreted in analog– e.g. traditional analog
implementations
TI Information – Selective Disclosure
Analog-digital interface
• Since the transition from analog domain to digital domain is expensive, only transmit what is necessary.– Maximize information content of each digital bit
– Minimize transfer of redundant information
• Analog sensor interface– Objective
• Operate analog circuits in high efficiency regime for low-power performance
• Integrated local analog signal processing circuitry results in sparse data being transferred to the digital domain
– Extract features of interest from sensors in the analog domain
– Transmit as digital events to the digital domain
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0 1 0 0 1 0
meaning?
Analog to digital encoding
TI Information – Selective Disclosure
Event-based sensing approach
• Each digital event encodes a feature of interest from the sensor– Event encoding
• Feature selection– Select what is and is not a feature from
sensor data– Decide what feature information to
transmit for each event (i.e. spatial position, temporal position, etc.)
– Event decoding• Digital processor must now interpret and
understand what each event means
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0 1 0 0 1 0
Describes features of object as time-based digital events
Analog to digital encoding
TI Information – Selective Disclosure
Dynamic vision sensor (DVS)
• Frame-free image (scene) processing– Only transmits individual pixel information when
has a change in relative log intensity
• Characteristics– Low bandwidth
– Low power consumption
– Low computational requirements
– High sensor dynamic range
• Technical specifications– 128x128 resolution, 120dB dynamic range,
23mW power consumption, 2.1% contrast threshold mismatch, 15us latency
• http://www.youtube.com/embed/5NNoq1Gq4sc
Lichtsteiner, et. al. (ISSCC 2006, JSSC 2008)
TI Information – Selective Disclosure
A silicon retina that reproduces signals in the optic nerve
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Zaghloul, et. al. (J. Neural Eng. 2006)
• Frame-free image (scene) processing– Only transmits individual pixel
information when has a change in relative log intensity
• Event decoding scheme– ON activity corresponds to bright
pixels and OFF activity corresponds to dark pixels
• Technical specifications– <100mW power consumption,
3.5mm x 3.3 mm
TI Information – Selective Disclosure
Convolution chips for image processing
• Event-based image processing– Frame-free event-based image
processing of asynchronous events– On-the-fly processing of events results in
2-D filtered version of the input flow
• Characteristics– Arbitrary kernel size and shape
• Technical specifications– 32x32 pixel 2-D convolution event
processor, 155ns event latency between output and input, 20Meps input rate, 45 Meps output rate, 350nm CMOS, 4.3x5.4mm2, 200mW at maximum kernel size and maximum input event rate
Linares-Barranco, et. al. (TCAS 2011)
TI Information – Selective Disclosure
Silicon cochlea architecture
Chan, et. al. (TCAS I 2007)
Seek to emulate cochlea performance and functionality by emulating cochlea biological architecture in silicon
-2nd order LPF bank
-Transform into analog signal
-Transform into “digital” neural event signal
Input sound
Digital events-Each “event” is a data packet describing event source (LPF) and event time
TI Information – Selective Disclosure
Reconstructed silicon cochlea data
time
cha
nn
el n
um
be
rSilicon cochlea
PC
Input sound
Digital events
PC reconstructs the output digital event information by sorting by channel (LPF) number and then aligning according to time stamp information.
TI Information – Selective Disclosure
750 Hz pure tone
Example data with pure tones (for one channel)
300 Hz pure tone
Simple real-time data processing procedure
•Count the time difference between events (interspike interval, ISI) for each channel
•Arrange the ISIs into a histogram
•A peak in the ISI histogram indicates a resonant frequency response
cha
nn
el n
um
be
r
cha
nn
el n
um
be
r
time time
bin
co
un
t
bin
co
un
t
ISI ISI
TI Information – Selective Disclosure
Sound Discrimination Example
“coo” sound “hiss” sound
Wav file
FFT
ISI histogram
TI Information – Selective Disclosure
3-D integrated silicon neuromorphic processor
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• 65,000, two-compartment neurons– Conductance-based integrate and fire
array transceiver (IFAT)• 65 million, 32-bit “virtual” synapses
– Conductance-based dynamical synapses– Dynamic table-look in embedded
memory (2Gb DRAM)• Locally dense, globally sparse synaptic
interconnectivity– Hierarchical address-event routing
(HiAER)– Dynamically reconfigurable– Asynchronous spike event I/O interface
Sender
Receiver
5 m
m
5 mm
5 m
m
5 mm
DRAMHiAER (Digital CMOS)IFAT (Analog CMOS)
Top metal
TSVTop metal
I/O pad
HiAER IFAT0.13μm CMOS 0.13μm CMOS
Hierarchical address-event routing (HiAER)
Park, et. al. (ISCAS 2012)
TI Information – Selective Disclosure
Theodore Yu UCSD Integrated Systems Neuroengineering Lab
Event-driven framework
• Coincidence detection performs efficient spike-based computation
– coincidence detection• two or more arriving events result in a stronger
response than a single arriving event
– applications• event-driven sensing
– sensors are only “on” when something important happens
• event-driven computation– information is sparsely represented with events
Yu, et. al. (EMBC 2012)
Provide background on motivation
Event-based approach relies upon temporal encoding to communicate signals.
The time of the event is the key parameter, not the voltage value. Event-encoding is robust against additive noise.
TI Information – Selective Disclosure
Temporal code and synchrony
• At a local scale, neurons perform coincidence detection within temporal integration window.
• At a network scale, the temporal delay information in events models the spatial distribution between neurons.
– Each scene of interest can be encoded as a unique combination of features
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10ms delay
5ms delay
4ms delay
Coincidence?Yes or no?
Input pattern
TI Information – Selective Disclosure
Temporal code and synchrony example
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10ms delay
5ms delay
4ms delay
Coincidence?Yes!
10ms delay
5ms delay
4ms delay
Coincidence?No!
Event at t = 3ms
Event at t = 8ms
Event at t = 7ms
Event at t = 2ms
Event at t = 8ms
Event at t = 7ms
TI Information – Selective Disclosure
Summary
• Analog event-based sensing– Since the transition from analog domain to digital domain
is expensive, only transmit what is necessary.• Maximize information content of each digital event through
encoding of features in analog domain• Minimize transfer of redundant information for sparse digital
signal processing
– Applications• Visual and acoustic sensors for event-encoding of features• Event-based processor performs event-decoding of features
utilizing coincidence detection in neural synchrony
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TI Information – Selective Disclosure 19
Thank you