Neuromorphic Enginerring- subthrshold design

CSEM, E. Vittoz/ GE99, Alghero, June 11, 1999 - 1

BIO-INSPIRED ANALOG VLSI CIRCUITS

AND THEIR INDUSTRIAL APPLICATION

Eric VittozCSEM Centre Suisse d'Electronique et de Microtechnique SA

CH-2007 NEUCHATEL, Switzerland

n Biology-inspiration

n Examples of industrial application

n Ongoing research

n Conclusion.


BIOLOGY AS A SOURCE OF INSPIRATION

In the continous fight for survival all along evolution:

n life has invented increasingly complex solutions

Biology inspired systems:

n borrow some of them

n adapt to constraints of available technologies

Inspiration for VLSI processing circuits: from the brain

Different levels:

n Representation of signals and informationn Strategiesn Architecturesn Methodology


METHODOLOGY

n Opportunism:

Along evolution, life has made, step after step:- best possible use of existing strucures as...- bootstrap for the next level of compexity.

Bottom-up approach, can be applied to VLSI:

- identify all properties of technologies, devices, circuits

- exploit them opportunistically to build more complex systems.

Also:

- explore potential of a solution in a broader range

- search for hidden advantages of apparent drawbacks.


POSSIBLE FUNCTIONS OF MOS TRANSISTORS

OPPORTUNISM:

n Switch (only function used in digital).

n Voltage-controlled conductance.

n Voltage-controlled current source.

n Multiplier (I D ~ VD x VG + terms).

n Functions x 2, sqrt(x) (strong inversion: IDsat ~ VG2 + terms).

n Functions e x, ln(x) (weak inversion: IDsat ~ eVG/nUT ).

n Current memory (open or floating gate).

n Bipolar operation:- precise e x, ln(x)- voltage and temperature references.

n Light sensor: any junction.

Can only be exploited in analog circuits.


PROSPECTS FOR ANALOG IN SIGNAL PROCESSING

0 30 60 90 120

10-9

10-12

10-15

10-18

SNR [dB]

Analog

Pmin/f [J]

Digital:

digital is moreefficient for “traditional”

processing

analog interfaces

(also, qualitatively: precision, linearity)

(also, qualitatively: chip area)

analogfor

perception n Goal: - make a decision- control an action

n Collective processing in massively parallel systems.

n Point of view of power consumption:


Π ISi

Π ISi

Π Ii

Π Ii

cw cw

ccwccw

= = λ

Ii

VBEi

n With bipolar transistors:

ΣVBEi = ΣVBEicw ccw

Ii = Isi eVBEiUTwith:

thus: VBEi = UT lnIiIsi

which yields:

cw = clockwiseccw = counter-clockwise

TRANSLINEAR CIRCUITS

n With MOS transistors in weak inversion:

i poor precision acceptable for perceptive processing.

[Gilbert, 1975]


EXAMPLE OF TRANSLINEAR CIRCUITS

VECTOR-LENGTH CALCULATION

I1

-

[Gilbert, 1975]

Iout = Ii2Σ

I1

+ + +

+p-MOS in weak inversion

Current mode, provide:

npn bipolar


PSEUDO-CONDUCTANCE/RESISTANCE

G1 G2

I1 I2I

G1* G2*I1 I2

I

for currents

n Linear behaviour of transistors with respect to currents [Bult, 1992]

n General case (any current level): same gate voltage

n Special case of weak inversion ("diffusor" [Boahen, 1992]):

i different values of gate voltages possible, thus...

i control of Gi* by gate voltage, or by...

i control current IC:

n Thus: any network of linear controlled resistors can be implemented by transistors only.

ICG*~ IC

conductances Gi pseudo-conductances Gi*~W/L


n Massive parallelism (10 11 neurons in brain)

VLSI technologies: towards billion-transistor chips

- large arrays (102-106) of...

- simple cells (10 - 103 tr.)

- low local speed (< 10kHz)

- limited local precision thanks to...

n Collective computation

- of all cells on the best solution

- dense interconnections(102 to 105 synapses/neuron)

ARCHITECTURES


EXAMPLES OF COLLECTIVE OPERATORS

n Normalization [Gilbert, 1984]

Σ Iouti = Itot

cell iItot

IoutiIini

n Winner-take-all [Lazzaro et al., 1988]

I0+

-

Iini

cell i

IoutiIinimax

i current gain same for all cells

i adjusts for:

Iouti =I0 for largest Iini0 for others

i points on largest current:

i value of largest current

n Consensus achieved by communication through single wire.


COMMUNICATION

n High connectivity is needed for collective computation but is a...

n Major obstacle for implementations in 2-dimensional VLSI.

n Possible (complementary) solutions:

i collective computation via single or few wire(s)

i communication with nearest neighbours only

i hierarchical interconnections (smaller density for larger distance)

i analog multiplexing:

- standard row/column scanning

- asynchronous and event driven

- pulse communication.


COMMUNICATION BY ADDRESS-CODING EVENTS

n Common m-wire bus.n Activity of each cell transmitted as...n Frequency of address-coding events:

code ofaddress

m-wirebus

event (set of very short pulses)

n Parallel access of all cells to the bus- with arbitration or...- without arbitration (random collisions)[Mortara, 1992]

n Features: - priority of access based on activity- no clock: phase of events is kept.

[Mahowald, 1992]


n Learning from examples (instead of programming)- on-chip learning not very useful in practice but...- learning on computer during design phase

resulting synaptic weights dumped on the chip.

n Adaptation

- in time (”high-pass”) , to eliminate constant information:novelty detectorelimination of constant spatial noise of sensors.

- to signal level, to cover wide dynamic range

140 dB of external excitation

40 dB of nerve activation

very useful for analog VLSI processing

STRATEGIES


I0 I0 I0

k

Iout(k)

G(k)

R(k)

(k)

F. (k)

pixel k-1 pixel k pixel k+1

RETINA FOR LOCAL ADAPTATION

n G(k) proportional to (k) (local photocurrent)

[Venier, 1997]

n If R(k) = 0 then ΣI out(k) =ΣI0 : global normalisation

n If R(k) >0, each I 0 distributed across area A ~ L2 = 1/(RG) ~ F

i thus: local normalisation in adjustable area A.

n Schematic for 1-D array

n Real implementation:

i 2-D array

i pseudo-conductances R*,G*

i 1/R(k) proportional to F. (k)


0

-20

-40

10 30

inte

nsi

ty d

B 44dB 16dB

20column

input current

output current

n Checker-board illuminated by light gradient:

n Experimental 35x35 hexagonal array of pixels.

n Output communication by address-coding events.

MEASUREMENTS ON LOCALLY ADAPTIVE RETINA

n Dynamic range reduced to that of the object contrast.


n Pulse representation (in biology: spikes of action potential)

- for communication

- for processing (audition...ubiquitous?)

time-domain processing

degree of coincidence of events

synchronization of processes

n "Place coding" (patterns of activity in 2D maps)

REPRESENTATION OF SIGNALS AND INFORMATION


PLACE CODING

Representation of a variable x:set of nodes with preferred values xi

0

1µi (activation grade of node i)

xx0 x1 x2 x3 x4 x5 x6 x7 x8

particular value

x y z

µi xiµi

x = ΣΣ

center of gravity

Computation of z = f(x, y) by network of links: each link is a fuzzy AND gate

Advantages:continuous amplitudes

high tolerance to perturbations

low power in current mode

systematic implementation of

[Landolt, 1998]

any function.


EXAMPLES OF

INDUSTRIAL APPLICATIONS

Biology-Inspired Analog VLSI:


DETECTION OF MAXIMUM SPOT INTENSITY

Control of LCD protection screen

Array of 26x26 pixels- light sensor- element of max.-current copier

18V generator, control and test

4x4.5 mm2 in 2µm process

Reacts in 50µs with 1mW.

[Chevroulet et al., 1995]


CALCULATION OF 1-DIMENSIONAL MOMENTS

R1

I1R2 Rj

IjRj+1 RN-1 RN

INI0

Ia Ibresistive

VR

RN*

IN

Ib

R1*

I2R2*

I0

Ia

IjRj* 0*0*

pseudo-resistive

x=0 x=1

n If: R j = jn-1 R1

n Then, n th order momentof current distribution:

Mxn = Ib

n Center of gravity x 0:

i n = 1 (Rj constant)

i x0 = Mx/M0 =Ib

Ia+ Ib


MONITORING OF SOLAR ILLUMINATION[Venier et al., 1996]

n Ambiance-control in vehicles- presence of sun- sun intensity- sun azimuth and elevation

n Array of 1365 pixels- organized in polar coordinates- light sensor- weighted copies of photocurrents

n Two linear pseudo-resistive neworks- radial- angular

n Reacts in 1ms for 100µA

n Precision better than 15°.


MOTION DETECTOR FOR TRACKBALL

n Inspired from rabbit’s eye

n Ball with random pattern of dots

n Illuminated by periodic flashes


E E EE E EE E EE E E

time ttime t+∆t

x (or y)p∆x(or ∆y)

row of sensors

MOTION DETECTOR FOR POINTING DEVICE

n Calculation of the 2 comp. of translation of a random pattern of dots.n detection of horizontal (and vertical) edges E :

n identification of edges moving during ∆t (between 2 snapshots)

n Motion during ∆t:

n current mode analog processing

n array of 75 cells, pitch p =300µm

n resolution above 800 dpi

n graceful degradation for defective cells.

∆x (or ∆y) = p*number of moving edges E

total number of edges

[Arreguit et al.,1996]


OPTICAL CHARACTER RECOGNITION

67 89 0

1 23 45........

output[Masa et al., 1999]

n Convolutive neural network.

n abstraction level increases at each step.

n All cells in same sub-layer haven same set of weights with shifted receptive field

thus: shift invariant.

n All cells in same column have samereceptive field.

n Weights learned during design

n implemented as capacitance ratios


THREE-LAYER CONVOLUTIONAL NEURAL NETWORK

n 100K capacitors ( 50K weights)

n 6x7mm 2 in 0.5 µm process

n Power consumption: <4mW

n Speed: 1000 images/s.

n Equiv. 10 8 multiply and add/sec.

n Wide range of- fonts- quality of printing

n Graceful degradation with defects.

[Masa et al., 1999]


ONGOING RESEARCH

Biology-Inspired Analog VLSI:


edgesenhanced

vertical

orientation enhanced

FEATURE ENHANCEMENT

n Edge enhancement by diffusion in linear network (in retinal layer).

n Orientation enhancement by nonlinear unisotropic diffusion:

n Address-coding event communication to...

Out

InIin

V+

V60oV0o

I0

V120o

i preferred orientation electrically adjustable.

60o

photo-receptors

i one cell:

[Venier et al., 1997]


current state

updates

retina

fixed wide-angle lens

prism gratings saliencymap

visualperceptionlens

sensor

motors

sensors

retina

saccadescontrolTrackingcontrol

final user orfurther processing

OCCULO-MOTOR SYSTEM

n Retina with narrow-field lens delivers high-resolution visual info.

i moving optical front end (rotating prisms) to change gaze angle.n Retina with wide-angle watches the whole visual field.

i saliency map (degree of interest of various parts in scene).

n Chip controls saccadic exploration of interesting parts (targets).n Chip centers and tracks selected target in closed loop.


CHIP FOR SACCADES CONTROL[Landolt, 1997]

ϕ1 ϕ2

rotating prisms

R

n

n n

n

target

visual field of retina

n Computes ϕ 1,ϕ2 =f(R) to center visual field of retina on target.

n Exploration time of targets function of their degree of interest n nn n

n Place-coding + communication by address-coding events.

n 160’000 transistors, 15’000 capacitors on 45mm 2 (0.5µm process).n Total power consumption at 5V: 5mW.


ϕ1 ∆ϕ1ϕ2

∆ϕ2

rotating prisms

CHIP FOR VISUAL TO MOTOR MAPPING

n Incremental control: computes ∆ϕ 1,∆ϕ2 = f(ϕ1,ϕ2, r) needed for ∆r

n Place coding; activation grade represented by :

i current (internal): 0...100nA

i pulse frequency (external): 0...1KHz

n Total power consumption at 5V: 30µW

nr∆r

visual fieldof retina

target[Landolt, 1997]Area: 2mm2


2R*

R*Iout

A1 An

bumpVi11Vr11

bumpVin1Vr1n

bumpVinm

Vrnmbump

Vi1m

Vr1m

I0

I1 In

n rules

m in

pu

t va

riab

les

~MIN

current splitters

4-bit weightingof activities

(activity of rule n)

1 outputVi Vr

+

ANALOG FUZZY CONTROLLER[Landolt, 1996]

R* R*

R* R*

bump circuit(input membership function)


FUZZY CONTROLLER CHIP[Landolt, 1996]

n Rule array: 8 by 10

n Supply voltage: 1.8V

n Power cons.: 850nW

n Input full scale: 1V

n Settling time: 0.5ms

n Accuracy: 2.6% RMS

n CMOS process: 2µm

n Chip core area: 1.2x1.1mm 2


n Further exploration of existing techniques

i identification of new applications

n Exploration of new aspects:

i pulse processing (events in time)

i stochastic processing

i oscillatory behaviour of arrays of nonlinear cells

i ......


i i iii i

i

i

iii

i

i i i i iiii i

iii i

ii i i

i

ii i i i ii

iii i i

ii i

i

i i ii i

0

50

100

130

iiiii

i i iii ii i i i

i ii i i i

iiiiii i i

ii i

iii i i i

iii i i

i ii i

i

i

ii

ii i ii

100

120

140

160

1 11 21 31 41 51array position

spik

es/

seco

nd

free-running (f0)

synchronization f = fM

output of coincidence array(fM = 130Hz)

i Experimental results:

EXAMPLE OF PULSE (SPIKE) PROCESSING

n Extraction of voice pitch (frequency of amplitude modulation)

1/f0

1/f

integration

effect of input

pulse

i Behaviour of “chopper cell”:

i System architecture [van Schaik, 1998]:silicon cochlea

1/fM

low fM high fM

long TR short TRchopper array

coincidence detector arrayi Application to voice separation?

refractory TR


n Biology-inspired systems based on analog VLSI:n still in infancyn slowly growing

n Potential already demonstrated by working chips:

n most of them still exploratoryn some have already resulted in innovative products.

n Very attractive for low-power smart microsystems

n in particular for low-level vision.

n May later compete with digital computation

n even when power is not limitedn for complex perception tasks.

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Neuromorphic Enginerring- subthrshold design