ieee santa clara comsoc/cas weekend workshop – event-based analog sensing

TI Information – Selective Disclosure

IEEE Santa Clara ComSoc/CAS Weekend Workshop – Event-based analog sensingTheodore Yu

[email protected]

Texas Instruments – Kilby Labs, Silicon Valley Labs

September 29, 2012

1


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

2


Analog-digital interface

3

• 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


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

4

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


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

5

0 1 0 0 1 0

meaning?

Analog to digital encoding


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

6

0 1 0 0 1 0

Describes features of object as time-based digital events

Analog to digital encoding


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)


A silicon retina that reproduces signals in the optic nerve

8

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


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)


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


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.


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


Sound Discrimination Example

“coo” sound “hiss” sound

Wav file

FFT

ISI histogram


3-D integrated silicon neuromorphic processor

14

• 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)


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.


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

16

10ms delay

5ms delay

4ms delay

Coincidence?Yes or no?

Input pattern


Temporal code and synchrony example

17

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


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

18

TI Information – Selective Disclosure 19

Thank you

ieee santa clara comsoc/cas weekend workshop – event-based analog sensing

Documents