1. Conditionally Evolving Memory for Computers W. A. C. Weerakoon#1, A. S
Conditionally Evolving Memory for Computers W.A.C. Weerakoon#1, A.S. Karunananda#2, N.G.J. Dias*3
Introduction
In Von-Neumann architecture, the memory improves processing power of the computer. Different kinds of smaller memories such as registers and caches were introduced to enhance the processing power.
There is a trend to design computing models by imitating human mind to improve the performance of the computer.
Process of executing the programs by the existing computing models is differ from how the human mind actually execute tasks: Human mind continuously gain improvements in quality, accuracy and efficiency in its processing while executing the task over generations with a support of a memory which is equivalent to the short notes used by the students in their studies.
Through the practice all the skill workers such as lecturers, doctors and carpenters improve their performance with a support of an evolving memory which consists of relevant tactics such as adding new knowledge, removing irrelevant knowledge and drawing relationships among knowledge entities, and finally become experts. In contrast, the computers do not show any difference in its processing eventhough it executes the same program multiple times.
This processing model which we see in human mind is what we are going to incorporate in to the computers through our research. 9 “
Karma
i. Asynchronous - Result is given after a long period of time.
ii. Co-nascent - Result will be given at the same time after finishing the process. 10
Continuously process clusters through resolution
Proximity
No interval between two processes 11
Co-nascence
Related things in a cluster are process together
Advanced Layout Instructions
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This particular template has a two-column layout, and guidelines have been placed at the centers of each of the three columns (0 inches from the center, 6.00 inches to the left of center, and 6.00 inches to the right of center). Use these guidelines to quickly position objects in a column.
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Advanced Figures/Graphics Instructions
Use vector graphics formats (preferably EPS or WMF) rather than raster graphics formats (PNG, JPG, BMP, etc.) when inserting charts or other non-photographic figures.
Keep your figures to 11 inches wide or smaller to avoid them crossing over more than one column.
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If you need a caption on a figure, insert a text box below the figure, resize the text box to no wider than 11 inches, and use PowerPoint’s Draw / Align or Distribute / Align Center menu at the bottom of the screen to ensure that the caption is centered under the image. Then use the Draw / Group menu to treat the figure and caption as a single entity. All the captions on this sample poster were created this way, and you can use them as examples if needed.
TABLE I: Mapping between the Buddhist concepts and the processing model
Turing Machine
T=(Q,∑,Γ,δ,q0,B,F), where
Q={q0, q1, q2, q3, q4, q5, q6, q7, q8}(Finite set of Internal States -All the Processing States) where q0= Initial/ Input, q1= Created, q2= Classified, q3= Added, q4= Discarded, q5=Activated, q6=Prioritized, q7=Resolved, q8=Terminated
∑={x0, x1, x2, x3, x4, x5, x6, x7, x8} (Input Alphabet–All the sub processes), where x0= Input, x1=Create, x2=Classify, x3=Add, x4=Discard, x5=Activate, x6=Prioritize, x7=Resolve, x8=No further improvements.
Γ= Tape Symbols, where ∑⊆Γ\{B}
δ = Transition function, where δ(q,X)=(p,Y,D), q∈Q is the current state, X∈Γ, p∈Q is the next state, Y∈Γ is the sub process which replaces the scanned sub process on the tape, D is the Direction of the head to move (left or right).
q0∈Q is the start state
B: Blank symbol- Initially input string is surrounded by blanks. After reaching the accepting state, nothing is on the tape except blank.
F=Accepting State, where F={q8}, q8∈Q
Process Model
Theoretical base is coming from the 24-causal relations explained in Buddhist theory of mind and work with a rule based approach. There we constructed a set of mappings between the 11-theoretical concepts extracted from the 24-causal relations. This mapping is showed in the TABLE I. The processing model is depicted in the FIGURE I. Further, for this a Turing machine showed in the FIGURE II has been designed. q0 q1 q2 q3 q4 q5 q6 q8 q7 x0 x1 x7 x8 x6 x5 x3 x2 x4
Origin
Classified
Pre-State
Terminate
Resolution
Identify & Create
facts /rules
Input
Add & Classify
Activate
Rules
No Improvements
Prioritize
Lack of relevant facts/ rules
or dominating input
Identify new & remove unnecessary rules
FIGURE II: Turing Machine
Discussion & Conclusion
The Boolean expression, which is derived from the above machine is:
𝑥 0 ⋀ 𝑥 1 ⋀ 𝑥 0 ∨ 𝑥 2 ⋀ 𝑥 0 ∨ 𝑥 3 ⋀ 𝑥 2 ∨𝑥 4 ∨𝑥 5 ∨ 𝑥 4 ⋀ 𝑥 2 ∨𝑥 3 ∨𝑥 𝑥 0 ∨ 𝑥 2 ⋀ 𝑥 0 ∨ 𝑥 3 ⋀ 𝑥 2 ∨𝑥 4 ∨𝑥 5 ∨ 𝑥 4 ⋀ 𝑥 2 ∨𝑥 3 ∨𝑥 ⋀ 𝑥 6 ⋀ 𝑥 0 ⋀𝑥 7 ∨ 𝑥 8
The truth table was drawn for the expression and the value “TRUE” was retrieved 48 times out of 512 possibilities. Therefore, we can conclude that this is satisfiable, hence this model can be validated as a problem in class NP.
Implement using C and CLIPS (C Language Integrated Production System)
With this we can easily handle slight changes and can expect a higher performance level.
FIGURE I: Process Model
New Processing Model
Condition
Purpose 1
Input/ Identify/ Create
Object
A process is initiated by an input 2
Classify
Root
Define the clusters on the basis of fundamentals of a domain to implement related things 3
Activate
Pre-nascence
An ongoing process conditions the formation of a new process 4
Addition
Post-nascence
A process can be improved by the next process. 5
Discarding/ Terminate
Absence
Remove unnecessary rules & facts/ A process can ceased 6
Prioritizing
Pre-Dominance
A process can be prioritized 7
Improve through repetition
Frequency
Repeated occurrence 8
Output
Karma-Result
Result is unobservable. (Karma-Vipaka)