Information Processing by Complex Thermodynamic Systems: A Search

for a New Computing Feasibility

Doy Sundarasaradula, Ph.D.

TOT Innovation Institute

TOT Public Company Limited, Thailand

IEEE-ICITIS 2011

Presentation Outline

• Limitations of traditional digital computing systems

• Levels of information

• Information processing & thermal energy conversion

• Equivalence between energy & information

• Dissipative structures

• Information processing by a dissipative structure

Limitations of traditional digital computing systems

• Traditional digital systems (e.g., digital computers)– Highly constrained

– Precisely laid out

– Not very fault tolerant

– Largely serial

– Centralised

– Deterministic– Minimally adaptive

Limitations of traditional digital computing systems

• Biological computing systems (e.g., brains)

– Massively parallel

– Densely connected with leaky transmission paths

– Fault tolerant

– Self repairing

– Adaptive

– Noisy and stochastic

Levels of information

• Syntactic (e.g., signs, symbols, etc.)

• Semantic (e.g., mutual understanding between senders and receivers)

• Pragmatic (e.g., mutual understanding between senders and receivers + mutual interactions)

Information processing & thermal energy conversion

Heat reservoir at high

temperature Th

Heat reservoir at

low temperature Tc

Heat engine

Qh

Qc

W = Qh - Qc

Figure 3. Working principle of heat engine based on Carnot theory

Non-Adaptive/ Rigid Structures

Information processing & thermal energy conversion

Less organised data

(in electrical energy

input or signaling

format, etc.)

Low quality

thermal

energy/entropy

dissipation

Microprocessor/

Microcontroller

Figure 4. Working principle of microprocessor-based information processing

systems

More useful and

organised information

(electrical signaling,

etc.)

Non-Adaptive/ Rigid Structures

Equivalence between energy & information

“According to the Shannon’s and Weaver’s information theory, a bit of information is equal to kln2 or approx. 10-23 joules per degree Kelvin, where k is Boltzmann’s Constant (Tribus and McIrvine, 1971).”

Dissipative Structures (Brusselators)

X Y

Figure 1. The cyclical organization of the Brusselator with an autocatalytic step of X.

A

B

E D

Adaptive/Interactive Structures

.

32

EX

DYXB

XYX

XA

XBXYXAdt

dX 2

YXBXdt

dY 2

Information processing by a dissipative structure

Figure 2. The dynamic of pragmatic information within a dissipative structure

BA

Novelty Confirmation

Autopoiesis

Level of P

ragm

atic info

rmation

Complete

ChaosComplete

Stagnation

Output signals generated by a Brusselator

10:50 30 Aug 2008

Figure 10.86 A test result - Flow Constant 5 is set at 0.00472

Page 1

0.00 4000.00 8000.00 12000.00 16000.00

Time

1:

1:

1:

0

10

20

1: X1

1 11

1

Output signals generated by a Brusselator

10:50 30 Aug 2008

Figure 10.87 A test result - Flow Constant 5 is set at 0.00472

Page 1

0.00 4000.00 8000.00 12000.00 16000.00

Time

1:

1:

1:

0

10

20

1: Y1

1

1

11

Influx affected by system’s activity

10:50 30 Aug 2008

Figure 10.88 A test result - Flow Constant 5 is set at 0.00472

Page 1

0.00 4000.00 8000.00 12000.00 16000.00

Time

1:

1:

1:

0

10

20

1: Source1

1 1 1 1

Influx affected by system’s activity

Dissipative systems

Energy fluxes from the

environment

Questions?

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