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Applications

Applications. t.k.prasad@wright.edu http://www.knoesis.org/tkprasad/. VHDL-Parser Pretty-Printer System. t.k.prasad@wright.edu http://www.knoesis.org/tkprasad/. URLs. Online Parser Documentation http://www.cs.wright.edu/~tkprasad/VHDL/VHDL-AMS/START.html Public Distribution

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Applications

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  1. Applications t.k.prasad@wright.edu http://www.knoesis.org/tkprasad/ L23-1-Applications

  2. VHDL-Parser Pretty-Printer System t.k.prasad@wright.edu http://www.knoesis.org/tkprasad/ L23-1-Applications

  3. URLs • Online Parser Documentation • http://www.cs.wright.edu/~tkprasad/VHDL/VHDL-AMS/START.html • Public Distribution • http://www.cs.wright.edu/~tkprasad/VHDL/VHDL-AMS.zip L23-1-Applications

  4. A Meta-Interpreter for circuit Extraction t.k.prasad@wright.edu http://www.knoesis.org/tkprasad/ L23-1-Applications

  5. Outline • Formal Description of Digital Circuits • Design Verification • Motivation for Applying Meta-programming Techniques • Implementation and Correctness Considerations • Conclusions L23-1-Applications

  6. Hierarchical Description of Circuit Design Full-Adder Half-Adder Sub-Components Abstraction AND-OR-NAND Gates Gate-level Description CMOS Transistors Transistor Netlist Component L23-1-Applications

  7. Declarative Specification of the Structure: Inverter and Netlist Single Inverter: inv(In,Out,X,Y) :- pt(In,vdd,Out,X,Y), nt(In,gnd,Out,_,_). Netlist: pt(in1,vdd,out1,50,50). nt(in1,gnd,out1,50,40). pt(out1,vdd,out2,100,50). nt(out1,gnd,out2,100,40). GND L23-1-Applications

  8. Problem and Solution Strategy • Verify structural correctness of a component layout by reverse engineering the top-level design. • Use automatically generated Prolog extraction rules • Prolog specifications are executable and can be used to simulate the circuit or check for faults by designing suitable queries L23-1-Applications

  9. Circuit Extraction MAGIC (CAD Tool) Layout TRANSLATE Netlist : Prolog Facts EXTRACT Higher-level Components L23-1-Applications

  10. Extraction Rules in Prolog extract_inverter :- pt(In,vdd,Out,X,Y), nt(In,gnd,Out,_,_), remove_pt(In,vdd,Out), remove_nt(In,gnd,Out), asserta(inverter(In,Out,X,Y)). L23-1-Applications

  11. Finer Points • Retracts not undone on backtracking => Identify complete component before retracting • Retract all occurrences of a component => Use “fail” appropriately. L23-1-Applications

  12. Spec vs Extraction Template component :- subcomponent_1 subcomponent_2. extract_component :- subcomponent_1 subcomponent_2, retract(subcomponent_1), retract(subcomponent_2), assert(component), fail. L23-1-Applications

  13. Problem and Our Solution • Requires generation of customized extraction rules (for each component) explicitly (which causes duplication of information). • Use meta-programming techniques to perform extraction “on-the-fly” using declarative specification of the structure of the component. L23-1-Applications

  14. Advantage: Avoids explicit creation and storing of extraction rules • Disadvantage: Meta-interpretation is slower than using customized extraction rules • Pragmatically: Explicit rules are good for extracting low-level components, while meta-interpreter is good for extracting high-level component extraction L23-1-Applications

  15. Meta-Rule extract(Comp) :- clause(Comp, Sub_Comps), Sub_Comps \== true, call(Sub_Comps), remove_primitive(Sub_Comps), asserta(Comp), fail. extract(_). L23-1-Applications

  16. remove_primitive((C1,C2)) :- !, remove_primitive(C1), remove_primitive(C2). remove_primitive(C) :- clause(C,true), !, retract(C). remove_primitive(C) :- clause(C,S_Cs), remove_primitive(S_Cs). L23-1-Applications

  17. (cont’d) • The interpreter does not work properly if the definition also contains ordinary predicates, written to capture connectivity constraints, position calculations, etc. • Introduce special meta-predicate constraint as follows: constraint(Test) : - call(Test). remove_primitive(constraint(_)):-!. L23-1-Applications

  18. Example invZ(P,N,I,O,X,Y) :- pt(I,vdd,Q,X1,Y1), pt(P,Q,O,X2,Y2), nt(N,O,R,X3,Y3), nt(I,R,gnd,X4,Y4), constraint( \+ connected([Q,R,vdd,gnd]) ), constraint( X is (X1+X2+X3+X4)/4, Y is (Y1+Y2+Y3+Y4)/4 ). L23-1-Applications

  19. Correctness Issue • Facts and rule-heads are disjoint. The lowest-level is represented as facts. Retraction of facts is sufficient. • Each fact contributes to just one rule. Subcomponents are not shared. Retraction of a fact does not interfere with the extraction of other components. (Stratification of sorts) L23-1-Applications

  20. Conclusion • Meta-interpreter approach uses the declarative specification of a design directly to perform extraction. • This approach is flexible for higher-level components. The trade-off is that it is inefficient for lower-level components. L23-1-Applications

  21. Meta-Interpreters Ref: Yoav Shoham’s AI Techniques in Prolog L23-1-Applications

  22. Types Extensions Expert systems Mycin Abductive Reasoning Diagnosis Annotated Logic Programming • Backward Chaining (Top-down) • Depth-first • Prolog • Breadth-first • Forward Chaining (Bottom-up) • Production Systems L23-1-Applications

  23. Representing forward chaining rules op(1000, xfy, ‘,’). op(1150, xfx, ‘-:’). p -: q. r,s -: p. q -: t. • Forward chaining adds new conclusions using rules in response to facts of the database and the newly asserted conclusions. L23-1-Applications

  24. Membership in and-list amember(X, (A,B)) :- !, ( X = A; amember(X,B)). amember(A,A). • Recall op(_, xfy, ‘,’). L23-1-Applications

  25. Propagating the effect of facts • Forward chaining interpreter recursively determines the conclusions forced by the facts and the rules, and asserts them explicitly. • Given the set of rules, each fact is asserted one by one (using the code shown on the next slide) and the conclusions implied by them are determined. • Specifically, the head of a rule is asserted after all the literals in the body of the rule have been asserted. L23-1-Applications

  26. Propagating the effects of facts update(X) :- clause(X, true),!. update(X) :- assert(X), ( If -: Then ), amember(X, If), \+((amember(Y, If), \+(clause(Y, true)))), update(Then), fail. L23-1-Applications

  27. Propagating the effect of facts • Complexity of update() • O( number of rules * number of body literals) • Can be optimized and extended to include negative literals, deletion, and maintaining justifications. • Reference: Chapter 4 of YoavShoham’s AI Techniques in Prolog L23-1-Applications

  28. Pooling of Evidence : The Mycin approach Ref: Yoav Shoham’s AI Techniques in Prolog L23-1-Applications

  29. Introducing Certainty Factor • Facts => CF = 1 • Rules => CF in [0,1] high_fever. (1) malaria :- high_fever, recently_in_jungle. (0.8) malaria :- … L23-1-Applications

  30. Mycin Interpreter cert(true, 1). cert( (A,B), C) :- !, cert(A , C1), cert(B, C2), comb_fn_serial(C1, C2, C). cert( A, C) :- !, findall(CR, (clause(A,B,CF), cert(B , CC), comb_fn_rule(CF,CC,CR)), CLst), comb_fn_parallel(CLst, C). L23-1-Applications

  31. Other Static Analysis Tools • Type Checking/Inference append(list(X), list(X), list(X)). fact(int, int). • Mode Inference append(+,+,_) L23-1-Applications

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