information processing by complex thermodynamic systems: a search for a new computing feasibility
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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?
Xie xie nin men!
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