particle swarm optimization for synthesis and dynamic ...€¦ · ¾the extrinsic environment...

P. Tawdross and A. Königslide:1

Particle Swarm Optimization for Synthesis and Dynamic Reconfiguration of Sensor Electronics

Peter Tawdross and Andreas König

Contents:Introduction Dynamic Reconfiguration ApproachTarget HardwareExperiments and ResultsConclusions

Institute of Integrated Sensor Systems

Dept. of Electrical Engineering and Information Technology


Sensor 1

Sensor 2

Sensor 4

Sensor 3

Sensor 5 SignalCondit.

ADC1

ADCn

Actor 1

Actor 2

DSP/Embedded

system

DAC

Introduction


Genetic algorithm or genetic programming design topologies and dimension them

To many operators, too many methods for each operator

Building arbitrary topologies with unpredictable behaviour

Starting from scratch after each environmental change as the current arbitrary topologies is not guaranteed to operate well at the new environment

The evaluation in the intrinsic level is the relation between the input and the output (no explicit spec. optimization)

Hard to find industrial acceptance

Dynamic Reconfiguration Approach State of the Art


≤∀

>∀−

=

ii

iii

ii

i

fspec

fspecspec

fspecE

0

≥∀

<∀−

=

ii

iii

ii

i

fspec

fspecspec

specfE

0

Min:Max:

The environment with multi-objective in the device level

Multi-objective

TsSR

Avo

offset

CMRR

CMRPSRR

PSO

ngspice/ Real chip

∑ ×= ii EHF

Ro

Pc

BWol BW3db

Swing

ϕ

PD

.....

Dynamic Reconfiguration ApproachThe Design Environment


The extrinsic environment

Dynamic Reconfiguration Approach The Extrinsic Design Environment

Optimization Library

Simulator SimulationLibrary

RequiredSpec.


RequiredSpec.





optimization library

PSO

HPSO

CPSOMSPSO

Other optimizationmethods

optimizationselector

Fitness functionSelector





Simulator SimulationLibrarysimulation

library

netlist generator

postprocessingobjectives

costaccumulator

RequiredSpec.





Simulator SimulationLibraryngspice

netlist files

output files

RequiredSpec.




RequiredSpec.



SIM: Block Type, Specification, and weights

Opt.: Optimization method, and parameters


The intrinsic environment

Dynamic Reconfiguration Approach The Intrinsic Design Environment

RequiredSpec.


Hardware IntrinsicLibraryIntrinsic

library

Bit-stream Gene.

postprocessingobjectives

costaccumulator


Hardware

RequiredSpec.


IntrinsicLibrary

The intrinsic environment

Dynamic Reconfiguration Approach The Intrinsic Design Environment

Measurement circuit

selector

Chip


The search space consists of n dimensions, each dimension represent a component (e.g., transistor, capacitor,...)

In the extrinsic approach switches are omitted and immediate aspectratio change by width modification is carried out.

In the intrinsic approach switches are implicitly includedEach transistor is programmable with integer value within the range of 1 to 257, which is the width of the transistorEach resistor or capacitor is programmable with integer value between 1 to 255

M1 M2 M3 M4 M5 M6........

Dynamic Reconfiguration ApproachRepresentation of the Optimization Problem


( ) ( )idt

idt

idt

idt

gdt

idt

idt

idt

idt

vxxxprandCxprandCvwv

11

211 ()()

++

+

+=

−××+−××+×=

For all the particles:

Sensing elements

Sensing elements are used to detect environmental changes

An action is taken after any environmental change detection

• e.g. Re-evaluate all the bests

• Re-initialize a part of the population, and set the current position as the best to the rest

Dynamic Reconfiguration Approach PSO Dynamic Approach


In addition to the standard technique advanced PSO methods are available, which we will study for their aptness in this application

There is more than one swarm in the optimisation

If two swarms are closed to each other, reinitialise one of them

Some particles are charged, charged particles dispel each other

Complexity f(N²)

( ) ( )

∑=

+

++

−×−

=

+−××+−××+×=N

j

jt

itj

tit

jiidt

it

it

gt

it

it

it

it

xxxx

QQa

axprCxprCvwv

131

1211

)(

()() rr

Dynamic Reconfiguration Approach Multi-swarm Particle Swarm Optimisation (MSPSO)


The particle formation is a hierarchical tree

Each particle fly to its best and to the best of the particle above it

If a particle best fitness better than the particle above it, swap them

3 21

7654

Dynamic Reconfiguration Approach Hierarchical Particle Swarm Optimisation (HPSO)

The Global best Particle

Worse than All the particle

above


The aspired generic sensor electronic front-end consists of basic reconfigurableblocks connected to each other with programmable switches

Target Hardware Basic Principle

Programmableswitches

Vout

-+Vin+

Vin-

Vout

-+Vin+

Vin-


Case Study: Fixed Topology, Miller-OpAmp as an example for our approach designed by S.K. LakshmananA Miller- structure has been investigated first in Austriamicrosystems 0.35 µm CMOS technology [S.K. Lakshmanan].

Shift-Register for simple interface in first implementation

8

3

2

1

w=4

w=1

w=2

w=128

Shift

-Reg

iste

r

Target HardwareReconfigurable Miller - OpAmp

M5

Gnd Gnd

RL

Vdd

Vin + Vin -

Vout

Bias

CC

M1 M2

M3 M4

M6M7


Experiments and ResultsExperimental Setup

DAQCard

CalibrationCircuits


Vout

-

+Vin+

Vin-

1010-4Ts↑1010-4Ts↓101.5CMR0.1

103103

Weights Hi

0.1m

104104

Speci

offset

SR ↓SR↑

The chip is optimized at room temperature for 20 iterations, then, heating of is 150°C appliedThe global best position is used as a sensing elementC1=2, C2=2, and w=1 [j. Kennedy 97]

Experiments and ResultsDevice Deviations


In the current setup, the temperature of the chip during the optimization is not exactly known Results are not repeatable

The temperature controlled oven should be used to heat all the chip to a known temperature

Experiments and ResultsIdeal Experiment


Experiments and ResultsExperimental Results

Measurement input Signal for one particle

Good Particle


0 20 40 60 80 100 120 140 160 180 200-4

-3

-2

-1

0

1

2

3

4

MSPSOHPSO

Converged fas ter at s tarting

More s table in dynamic environment

More rebels in dynamic environment

HPSO converged faster in static environment

MSPSO more stable in dynamic environment

Heating gradually to 150 °C

Average of 5 runs20 particle / population

Experiments and ResultsExperimental Results


Intrinsic evolution is applied to optimize operational amplifier according to an industrial specificationPSO is applied to optimize a reconfigurable operational amplifier in a dynamic environment

Reconfigurable analog electronics allows rapid prototyping and scalabilityInherent dynamic fault-tolerance and self-healing (-x) capability

The complete list of specification values of the components will be included in the intrinsic evolution in futureOur approach will be extended to different amplifier types and there application for signal conditioning in a generic sensor electronicsfront-end

Conclusions


Thanks

particle swarm optimization for synthesis and dynamic ...€¦ · ¾the extrinsic environment...

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